Bioinformatics: Difference between revisions

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[[File:WPP domain alignment.PNG|500px|thumbnail|right|Early bioinformatics—computational alignment of experimentally determined sequences of a class of related proteins; see {{Section link||Sequence analysis}} for further information.]]

[[Image:Genome viewer screenshot small.png|thumbnail|220px|Map of the human X chromosome (from the [[National Center for Biotechnology Information]] (NCBI) website)]]

'''Bioinformatics''' ({{IPAc-en|audio=en-us-bioinformatics.ogg|ˌ|b|aɪ|.|oʊ|ˌ|ɪ|n|f|ɚ|ˈ|m|æ|t|ɪ|k|s}}) is an [[interdisciplinary]] field of [[science]] that develops computational methods and [[Bioinformatics software|software tool]]s for understanding [[biological]] data, especially when the data sets are large and complex. Bioinformatics integrates principles from [[biology]], [[chemistry]], [[physics]], [[computer science]], [[data science]], [[computer programming]], [[information engineering]], [[mathematics]], and [[statistics]] to analyze and interpret [[biological data]].<ref name=":1" /> This process can sometimes be referred to as [[computational biology]]; however, the distinction between the two terms is often disputed. The term ''[[computational biology]]'' can refer to building and using models of [[Biological system|biological systems]].

'''Bioinformatics''' ({{IPAc-en|audio=en-us-bioinformatics.ogg|ˌ|b|aɪ|.|oʊ|ˌ|ɪ|n|f|ɚ|ˈ|m|æ|t|ɪ|k|s}}) is an [[interdisciplinary]] field of [[science]] that develops methods and [[Bioinformatics software|software tool]]s for understanding [[biological]] data, especially when the data sets are large and complex. Bioinformatics ~~uses~~ [[biology]], [[chemistry]], [[physics]], [[computer science]], [[data science]], [[computer programming]], [[information engineering]], [[mathematics]] and [[statistics]] to analyze and interpret [[biological data]]. This process can sometimes be referred to as [[computational biology]], ~~however~~ the distinction between the two terms is often disputed. ~~To some, the~~ term ''computational biology'' ~~refers~~ to building and using models of biological systems.

Computational, statistical, and computer programming techniques have been used for [[In silico|computer simulation]] analyses of biological queries. They include reused specific analysis "pipelines", particularly in the field of [[genomics]], such as by the identification of [[gene]]s and single [[nucleotide]] polymorphisms ([[SNPs]]). These pipelines are used to better understand the genetic basis of disease, unique adaptations, desirable properties (especially in agricultural species), or differences between populations. Bioinformatics also includes [[proteomics]], which aims to understand the organizational principles within [[nucleic acid]] and [[protein]] sequences.<ref>{{cite web |vauthors=Lesk AM |date=26 July 2013 |title=Bioinformatics |url=https://www.britannica.com/science/bioinformatics |website=Encyclopaedia Britannica |access-date=17 April 2017 |archive-date=14 April 2021 |archive-url=https://web.archive.org/web/20210414103621/https://www.britannica.com/science/bioinformatics |url-status=live }}</ref>

Image and [[signal processing]] allow extraction of useful results from large amounts of raw data. It aids in sequencing and annotating genomes and their observed [[mutation]]s. Bioinformatics includes [[text mining]] of biological literature and the development of biological and gene [[ontologies]] to organize and query biological data. It also plays a role in the analysis of gene and protein expression and regulation. Bioinformatic tools aid in comparing, analyzing, interpreting genetic and genomic data and in the understanding of evolutionary aspects of molecular biology. At a more integrative level, it helps analyze and catalogue the biological pathways and networks that are an important part of [[systems biology]]. In [[structural biology]], it aids in the simulation and modeling of DNA,<ref name=":0">{{cite journal | vauthors = Sim AY, Minary P, Levitt M | title = Modeling nucleic acids | journal = Current Opinion in Structural Biology | volume = 22 | issue = 3 | pages = 273–8 | date = June 2012 | pmid = 22538125 | pmc = 4028509 | doi = 10.1016/j.sbi.2012.03.012 }}</ref> RNA,<ref name=":0" /><ref>{{~~cite~~ journal | ~~vauthors~~ = ~~Dawson WK, Maciejczyk M, Jankowska EJ, Bujnicki JM~~ | title = ~~Coarse-grained modeling~~ of ~~RNA 3D structure~~ | journal = ~~Methods~~ | volume = ~~103~~ | ~~pages~~ = ~~138–56~~ | ~~date = July 2016 | pmid~~ = ~~27125734~~ | doi = 10.~~1016~~/j.~~ymeth~~.~~2016~~.04.~~026~~ | doi-access = free }}</ref> proteins<ref>{{cite journal | vauthors = Kmiecik S, Gront D, Kolinski M, Wieteska L, Dawid AE, Kolinski A | title = Coarse-Grained Protein Models and Their Applications | journal = Chemical Reviews | volume = 116 | issue = 14 | pages = 7898–936 | date = July 2016 | pmid = 27333362 | doi = 10.1021/acs.chemrev.6b00163 | doi-access = free }}</ref> as well as biomolecular interactions.<ref>{{cite book | vauthors = Wong KC |year=2016 |title=Computational Biology and Bioinformatics: Gene Regulation |publisher=CRC Press/Taylor & Francis Group |isbn=978-1-4987-2497-5 }}</ref><ref>{{cite journal | vauthors = Joyce AP, Zhang C, Bradley P, Havranek JJ | title = Structure-based modeling of protein: DNA specificity | journal = Briefings in Functional Genomics | volume = 14 | issue = 1 | pages = 39–49 | date = January 2015 | pmid = 25414269 | pmc = 4366589 | doi = 10.1093/bfgp/elu044 | doi-access = free }}</ref><ref>{{Cite book | vauthors = Spiga E, Degiacomi MT, Dal Peraro M |date=2014 |chapter=New Strategies for Integrative Dynamic Modeling of Macromolecular Assembly | veditors = Karabencheva-Christova T |title=Biomolecular Modelling and Simulations |series=Advances in Protein Chemistry and Structural Biology |volume=96 |pages=77–111 |publisher=Academic Press |doi=10.1016/bs.apcsb.2014.06.008 |pmid=25443955 |isbn=978-0-12-800013-7 }}</ref><ref>{{cite journal | vauthors = Ciemny M, Kurcinski M, Kamel K, Kolinski A, Alam N, Schueler-Furman O, Kmiecik S | title = Protein-peptide docking: opportunities and challenges | journal = Drug Discovery Today | volume = 23 | issue = 8 | pages = 1530–1537 | date = August 2018 | pmid = 29733895 | doi = 10.1016/j.drudis.2018.05.006 | doi-access = free }}</ref>

Image and [[signal processing]] allow the extraction of useful results from large amounts of raw data. It aids in sequencing and annotating genomes and their observed [[mutation]]s. Bioinformatics includes [[text mining]] of biological literature and the development of biological and gene [[ontologies]] to organize and query biological data. It also plays a role in the analysis of gene and protein expression and regulation. Bioinformatic tools aid in comparing, analyzing, and interpreting genetic and genomic data and in the understanding of evolutionary aspects of molecular biology. At a more integrative level, it helps analyze and catalogue the biological pathways and networks that are an important part of [[systems biology]]. In [[structural biology]], it aids in the simulation and modeling of DNA,<ref name=":0">{{cite journal | vauthors = Sim AY, Minary P, Levitt M | title = Modeling nucleic acids | journal = Current Opinion in Structural Biology | volume = 22 | issue = 3 | pages = 273–8 | date = June 2012 | pmid = 22538125 | pmc = 4028509 | doi = 10.1016/j.sbi.2012.03.012 }}</ref> RNA,<ref name=":0" /><ref name=":1">{{Cite journal |last1=Welch |first1=Lonnie |last2=Lewitter |first2=Fran |last3=Schwartz |first3=Russell |date=2014 |title=Bioinformatics Curriculum Guidelines: Toward a Definition of Core Competencies |journal=PLOS Comput Biol |volume=10 |issue=3 |article-number=e1003496 |doi=10.1371/journal.pcbi.1003496 |pmid=24603430 |pmc=3945096 |bibcode=2014PLSCB..10E3496W |doi-access=free }}</ref> proteins<ref>{{cite journal | vauthors = Kmiecik S, Gront D, Kolinski M, Wieteska L, Dawid AE, Kolinski A | title = Coarse-Grained Protein Models and Their Applications | journal = Chemical Reviews | volume = 116 | issue = 14 | pages = 7898–936 | date = July 2016 | pmid = 27333362 | doi = 10.1021/acs.chemrev.6b00163 | bibcode = 2016ChRv..116.7898K | doi-access = free }}</ref> as well as biomolecular interactions.<ref>{{cite book | vauthors = Wong KC |year=2016 |title=Computational Biology and Bioinformatics: Gene Regulation |publisher=CRC Press/Taylor & Francis Group |isbn=978-1-4987-2497-5 }}</ref><ref>{{cite journal | vauthors = Joyce AP, Zhang C, Bradley P, Havranek JJ | title = Structure-based modeling of protein: DNA specificity | journal = Briefings in Functional Genomics | volume = 14 | issue = 1 | pages = 39–49 | date = January 2015 | pmid = 25414269 | pmc = 4366589 | doi = 10.1093/bfgp/elu044 | doi-access = free }}</ref><ref>{{Cite book | vauthors = Spiga E, Degiacomi MT, Dal Peraro M |date=2014 |chapter=New Strategies for Integrative Dynamic Modeling of Macromolecular Assembly | veditors = Karabencheva-Christova T |title=Biomolecular Modelling and Simulations |series=Advances in Protein Chemistry and Structural Biology |volume=96 |pages=77–111 |publisher=Academic Press |doi=10.1016/bs.apcsb.2014.06.008 |pmid=25443955 |isbn=978-0-12-800013-7 |url=https://durham-repository.worktribe.com/output/1372599 }}</ref><ref>{{cite journal | vauthors = Ciemny M, Kurcinski M, Kamel K, Kolinski A, Alam N, Schueler-Furman O, Kmiecik S | title = Protein-peptide docking: opportunities and challenges | journal = Drug Discovery Today | volume = 23 | issue = 8 | pages = 1530–1537 | date = August 2018 | pmid = 29733895 | doi = 10.1016/j.drudis.2018.05.006 | doi-access = free }}</ref>

== History ==

==History==

The first definition of the term ''bioinformatics'' was coined by [[Paulien Hogeweg]] and [[Ben Hesper]] in 1970, to refer to the study of information processes in biotic systems.<ref>{{cite journal |last1=Ouzounis |first1=C. A. |last2=Valencia |first2=A. |date=2003 |title=Early bioinformatics: the birth of a discipline—a personal view |journal=Bioinformatics |volume=19 |issue=17 |pages=2176–2190 | pmid=14630646 | doi=10.1093/bioinformatics/btg309| doi-access=free}}</ref><ref name="Hogeweg2011">{{cite journal |vauthors=Hogeweg P |title=The Roots of Bioinformatics in Theoretical Biology |journal=PLOS Computational Biology |volume=7 |issue=3 |~~pages~~=e1002021 |date=2011 |pmid=21483479 |pmc=3068925 | doi=10.1371/journal.pcbi.1002021 | bibcode = 2011PLSCB...7E2021H | doi-access = free }}</ref><ref>{{Cite journal| vauthors = Hesper B, Hogeweg P |year=1970|title=BIO-INFORMATICA: een werkconcept |trans-title=BIO-INFORMATICS: a working concept |language=nl |journal=Het Kameleon|volume=1 |issue=6| pages=28–29}}</ref><ref>{{cite arXiv |vauthors=Hesper B, Hogeweg P |eprint=2111.11832v1 |title=Bio-informatics: a working concept. A translation of "Bio-informatica: een werkconcept" by B. Hesper and P. Hogeweg |date=2021 |class=q-bio.OT}}</ref><ref>{{cite journal |vauthors = Hogeweg P |title=Simulating the growth of cellular forms |journal=Simulation |volume=31 |issue=3 |pages=90–96 |year=1978 |doi=10.1177/003754977803100305 |s2cid=61206099 }}</ref> This definition placed bioinformatics as a field parallel to [[biochemistry]] (the study of chemical processes in biological systems).<ref name="Hogeweg2011" />

The first definition of the term ''bioinformatics'' was coined by [[Paulien Hogeweg]] and [[Ben Hesper]] in 1970, to refer to the study of information processes in biotic systems.<ref>{{cite journal |last1=Ouzounis |first1=C. A. |last2=Valencia |first2=A. |date=2003 |title=Early bioinformatics: the birth of a discipline—a personal view |journal=Bioinformatics |volume=19 |issue=17 |pages=2176–2190 | pmid=14630646 | doi=10.1093/bioinformatics/btg309| doi-access=free}}</ref><ref name="Hogeweg2011">{{cite journal |vauthors=Hogeweg P |title=The Roots of Bioinformatics in Theoretical Biology |journal=PLOS Computational Biology |volume=7 |issue=3 |article-number=e1002021 |date=2011 |pmid=21483479 |pmc=3068925 | doi=10.1371/journal.pcbi.1002021 | bibcode = 2011PLSCB...7E2021H | doi-access = free }}</ref><ref>{{Cite journal| vauthors = Hesper B, Hogeweg P |year=1970|title=BIO-INFORMATICA: een werkconcept |trans-title=BIO-INFORMATICS: a working concept |language=nl |journal=Het Kameleon|volume=1 |issue=6| pages=28–29}}</ref><ref>{{cite arXiv |vauthors=Hesper B, Hogeweg P |eprint=2111.11832v1 |title=Bio-informatics: a working concept. A translation of "Bio-informatica: een werkconcept" by B. Hesper and P. Hogeweg |date=2021 |class=q-bio.OT}}</ref><ref>{{cite journal |vauthors = Hogeweg P |title=Simulating the growth of cellular forms |journal=Simulation |volume=31 |issue=3 |pages=90–96 |year=1978 |doi=10.1177/003754977803100305 |s2cid=61206099 }}</ref> This definition placed bioinformatics as a field parallel to [[biochemistry]] (the study of chemical processes in biological systems).<ref name="Hogeweg2011" />

Bioinformatics and computational biology involved the analysis of biological data, particularly DNA, RNA, and protein sequences. The field of bioinformatics experienced explosive growth starting in the mid-1990s, driven largely by the [[Human Genome Project]] and by rapid advances in DNA sequencing technology.{{cn|date=February 2025}}

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Analyzing biological data to produce meaningful information involves writing and running software programs that use [[algorithm]]s from [[graph theory]], [[artificial intelligence]], [[soft computing]], [[data mining]], [[image processing]], and [[computer simulation]]. The algorithms in turn depend on theoretical foundations such as [[discrete mathematics]], [[control theory]], [[system theory]], [[information theory]], and [[statistics]].{{cn|date=May 2024}}

=== Sequences ===

===Sequences===

[[File: Example DNA sequence.png|thumbnail|right|Sequences of genetic material are frequently used in bioinformatics and are easier to manage using computers than manually.]]

[[File:Muscle alignment view.png|thumb|369x369px|These are sequences being compared in a MUSCLE multiple sequence alignment (MSA). Each sequence name (leftmost column) is from various louse species, while the sequences themselves are in the second column.]]

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In the 1970s, new techniques for sequencing DNA were applied to bacteriophage MS2 and øX174, and the extended nucleotide sequences were then parsed with informational and statistical algorithms. These studies illustrated that well known features, such as the coding segments and the triplet code, are revealed in straightforward statistical analyses and were the proof of the concept that bioinformatics would be insightful.<ref>{{cite journal | vauthors = Erickson JW, Altman GG |title=A Search for Patterns in the Nucleotide Sequence of the MS2 Genome |journal=Journal of Mathematical Biology |date=1979 |volume=7 |issue=3 |pages=219–230 |doi=10.1007/BF00275725 |s2cid=85199492 }}</ref><ref>{{cite journal | vauthors = Shulman MJ, Steinberg CM, Westmoreland N | title = The coding function of nucleotide sequences can be discerned by statistical analysis | journal = Journal of Theoretical Biology | volume = 88 | issue = 3 | pages = 409–20 | date = February 1981 | pmid = 6456380 | doi = 10.1016/0022-5193(81)90274-5 | bibcode = 1981JThBi..88..409S }}</ref>

== Goals ==

==Goals==

In order to study how normal cellular activities are altered in different disease states, raw biological data must be combined to form a comprehensive picture of these activities. Therefore{{When|date=June 2023}}, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. This also includes nucleotide and [[amino acid sequence]]s, [[protein domain]]s, and [[protein structure]]s.<ref>{{Cite book|title=Essential Bioinformatics|url=https://archive.org/details/essentialbioinfo00xion|url-access=limited| vauthors = Xiong J |publisher=Cambridge University Press|year=2006|isbn=978-0-511-16815-4|location=Cambridge, United Kingdom|pages=[https://archive.org/details/essentialbioinfo00xion/page/n13 4]|via=Internet Archive}}</ref>

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* Development of new mathematical algorithms and statistical measures to assess relationships among members of large data sets. For example, there are methods to locate a [[gene]] within a sequence, to predict protein structure and/or function, and to [[Cluster analysis|cluster]] protein sequences into families of related sequences.

The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other approaches is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include: [[pattern recognition]], [[data mining]], [[machine learning]] ~~algorithms~~, and [[Biological Data Visualization|visualization]]. Major research efforts in the field include [[sequence alignment]], [[gene finding]], [[genome assembly]], [[drug design]], [[drug discovery]], [[protein structural alignment|protein structure alignment]], [[protein structure prediction]], prediction of [[gene expression]] and [[protein–protein interaction]]s, [[genome-wide association studies]], the modeling of [[evolution]] and [[Cellular model|cell division/mitosis.]]

The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other approaches is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include: [[pattern recognition]], [[data mining]], [[machine learning in bioinformatics|machine learning algorithms]], and [[Biological Data Visualization|visualization]]. Major research efforts in the field include [[sequence alignment]], [[gene finding]], [[genome assembly]], [[drug design]], [[drug discovery]], [[protein structural alignment|protein structure alignment]], [[protein structure prediction]], prediction of [[gene expression]] and [[protein–protein interaction]]s, [[genome-wide association studies]], the modeling of [[evolution]] and [[Cellular model|cell division/mitosis.]]

Bioinformatics entails the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the management and analysis of biological data.

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Following the goals that the Human Genome Project left to achieve after its closure in 2003, the [[ENCODE]] project was developed by the [[National Human Genome Research Institute]]. This project is a collaborative data collection of the functional elements of the human genome that uses next-generation DNA-sequencing technologies and genomic tiling arrays, technologies able to automatically generate large amounts of data at a dramatically reduced per-base cost but with the same accuracy (base call error) and fidelity (assembly error).

==== Gene function prediction ====

====Gene function prediction====

While genome annotation is primarily based on sequence similarity (and thus [[Homology (biology)|homology]]), other properties of sequences can be used to predict the function of genes. In fact, most ''gene'' function prediction methods focus on ''protein'' sequences as they are more informative and more feature-rich. For instance, the distribution of hydrophobic [[amino acid]]s predicts [[Transmembrane domain|transmembrane segments]] in proteins. However, protein function prediction can also use external information such as gene (or protein) [[Gene expression|expression]] data, [[protein structure]], or [[~~protein-protein interactions~~]].<ref>{{cite journal |vauthors=Erdin S, Lisewski AM, Lichtarge O |title=Protein function prediction: towards integration of similarity metrics |journal=Current Opinion in Structural Biology |volume=21 |issue=2 |pages=180–8 |date=April 2011 |pmid=21353529 |pmc=3120633 |doi=10.1016/j.sbi.2011.02.001}}</ref>

While genome annotation is primarily based on sequence similarity (and thus [[Homology (biology)|homology]]), other properties of sequences can be used to predict the function of genes. In fact, most ''gene'' function prediction methods focus on ''protein'' sequences as they are more informative and more feature-rich. For instance, the distribution of hydrophobic [[amino acid]]s predicts [[Transmembrane domain|transmembrane segments]] in proteins. However, protein function prediction can also use external information such as gene (or protein) [[Gene expression|expression]] data, [[protein structure]], or [[protein–protein interaction]]s.<ref>{{cite journal |vauthors=Erdin S, Lisewski AM, Lichtarge O |title=Protein function prediction: towards integration of similarity metrics |journal=Current Opinion in Structural Biology |volume=21 |issue=2 |pages=180–8 |date=April 2011 |pmid=21353529 |pmc=3120633 |doi=10.1016/j.sbi.2011.02.001}}</ref>

===Computational evolutionary biology===

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Pan genomics is a concept introduced in 2005 by Tettelin and Medini. Pan genome is the complete gene repertoire of a particular [[monophyletic]] taxonomic group. Although initially applied to closely related strains of a species, it can be applied to a larger context like genus, phylum, etc. It is divided in two parts: the Core genome, a set of genes common to all the genomes under study (often housekeeping genes vital for survival), and the Dispensable/Flexible genome: a set of genes not present in all but one or some genomes under study. A bioinformatics tool BPGA can be used to characterize the Pan Genome of bacterial species.<ref>{{cite journal | vauthors = Chaudhari NM, Gupta VK, Dutta C | title = BPGA- an ultra-fast pan-genome analysis pipeline | journal = Scientific Reports | volume = 6 | ~~pages~~ = 24373 | date = April 2016 | pmid = 27071527 | pmc = 4829868 | doi = 10.1038/srep24373 | bibcode = 2016NatSR...624373C }}</ref>

Pan genomics is a concept introduced in 2005 by Tettelin and Medini. Pan genome is the complete gene repertoire of a particular [[monophyletic]] taxonomic group. Although initially applied to closely related strains of a species, it can be applied to a larger context like genus, phylum, etc. It is divided in two parts: the Core genome, a set of genes common to all the genomes under study (often housekeeping genes vital for survival), and the Dispensable/Flexible genome: a set of genes not present in all but one or some genomes under study. A bioinformatics tool BPGA can be used to characterize the Pan Genome of bacterial species.<ref>{{cite journal | vauthors = Chaudhari NM, Gupta VK, Dutta C | title = BPGA- an ultra-fast pan-genome analysis pipeline | journal = Scientific Reports | volume = 6 | article-number = 24373 | date = April 2016 | pmid = 27071527 | pmc = 4829868 | doi = 10.1038/srep24373 | bibcode = 2016NatSR...624373C }}</ref>

===Genetics of disease===

As of 2013, the existence of efficient high-throughput next-generation sequencing technology allows for the identification of cause many different human disorders. Simple [[Mendelian inheritance]] has been observed for over 3,000 disorders that have been identified at the [[Online Mendelian Inheritance in Man]] database, but complex diseases are more difficult. Association studies have found many individual genetic regions that individually are weakly associated with complex diseases (such as [[infertility]],<ref name="Demerec1945">{{cite journal | vauthors = Aston KI | title = Genetic susceptibility to male infertility: news from genome-wide association studies | journal = Andrology | volume = 2 | issue = 3 | pages = 315–21 | date = May 2014 | pmid = 24574159 | doi = 10.1111/j.2047-2927.2014.00188.x | s2cid = 206007180 | doi-access = free }}</ref> [[breast cancer]]<ref name="Véron2013">{{cite journal | vauthors = Véron A, Blein S, Cox DG | title = Genome-wide association studies and the clinic: a focus on breast cancer | journal = Biomarkers in Medicine | volume = 8 | issue = 2 | pages = 287–96 | year = 2014 | pmid = 24521025 | doi = 10.2217/bmm.13.121 }}</ref> and [[Alzheimer's disease]]<ref name="Tosto2013">{{cite journal | vauthors = Tosto G, Reitz C | title = Genome-wide association studies in Alzheimer's disease: a review | journal = Current Neurology and Neuroscience Reports | volume = 13 | issue = 10 | ~~pages~~ = 381 | date = October 2013 | pmid = 23954969 | pmc = 3809844 | doi = 10.1007/s11910-013-0381-0 }}</ref>), rather than a single cause.<ref name="Londin2013">{{Cite book |vauthors=Londin E, Yadav P, Surrey S, Kricka LJ, Fortina P |chapter=Use of linkage analysis, genome-wide association studies, and next-generation sequencing in the identification of disease-causing mutations |title=Pharmacogenomics |volume=1015 |pages=127–46 |year=2013 |pmid=23824853 |doi=10.1007/978-1-62703-435-7_8 |isbn=978-1-62703-434-0 |series=Methods in Molecular Biology }}</ref><ref>{{cite journal | vauthors = Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA | title = Potential etiologic and functional implications of genome-wide association loci for human diseases and traits | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 106 | issue = 23 | pages = 9362–7 | date = June 2009 | pmid = 19474294 | pmc = 2687147 | doi = 10.1073/pnas.0903103106 | doi-access = free | bibcode = 2009PNAS..106.9362H }}</ref> There are currently many challenges to using genes for diagnosis and treatment, such as how we don't know which genes are important, or how stable the choices an algorithm provides.<ref>{{Cite book | vauthors = Hall LO |title=2010 International Conference on System Science and Engineering |chapter=Finding the right genes for disease and prognosis prediction |date=2010 |pages=1–2 |doi=10.1109/ICSSE.2010.5551766|isbn=978-1-4244-6472-2 |s2cid=21622726 }}</ref>

As of 2013, the existence of efficient high-throughput next-generation sequencing technology allows for the identification of cause many different human disorders. Simple [[Mendelian inheritance]] has been observed for over 3,000 disorders that have been identified at the [[Online Mendelian Inheritance in Man]] database, but complex diseases are more difficult. Association studies have found many individual genetic regions that individually are weakly associated with complex diseases (such as [[infertility]],<ref name="Demerec1945">{{cite journal | vauthors = Aston KI | title = Genetic susceptibility to male infertility: news from genome-wide association studies | journal = Andrology | volume = 2 | issue = 3 | pages = 315–21 | date = May 2014 | pmid = 24574159 | doi = 10.1111/j.2047-2927.2014.00188.x | s2cid = 206007180 | doi-access = free }}</ref> [[breast cancer]]<ref name="Véron2013">{{cite journal | vauthors = Véron A, Blein S, Cox DG | title = Genome-wide association studies and the clinic: a focus on breast cancer | journal = Biomarkers in Medicine | volume = 8 | issue = 2 | pages = 287–96 | year = 2014 | pmid = 24521025 | doi = 10.2217/bmm.13.121 }}</ref> and [[Alzheimer's disease]]<ref name="Tosto2013">{{cite journal | vauthors = Tosto G, Reitz C | title = Genome-wide association studies in Alzheimer's disease: a review | journal = Current Neurology and Neuroscience Reports | volume = 13 | issue = 10 | article-number = 381 | date = October 2013 | pmid = 23954969 | pmc = 3809844 | doi = 10.1007/s11910-013-0381-0 }}</ref>), rather than a single cause.<ref name="Londin2013">{{Cite book |vauthors=Londin E, Yadav P, Surrey S, Kricka LJ, Fortina P |chapter=Use of linkage analysis, genome-wide association studies, and next-generation sequencing in the identification of disease-causing mutations |title=Pharmacogenomics |volume=1015 |pages=127–46 |year=2013 |pmid=23824853 |doi=10.1007/978-1-62703-435-7_8 |isbn=978-1-62703-434-0 |series=Methods in Molecular Biology }}</ref><ref>{{cite journal | vauthors = Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA | title = Potential etiologic and functional implications of genome-wide association loci for human diseases and traits | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 106 | issue = 23 | pages = 9362–7 | date = June 2009 | pmid = 19474294 | pmc = 2687147 | doi = 10.1073/pnas.0903103106 | doi-access = free | bibcode = 2009PNAS..106.9362H }}</ref> There are currently many challenges to using genes for diagnosis and treatment, such as how we don't know which genes are important, or how stable the choices an algorithm provides.<ref>{{Cite book | vauthors = Hall LO |title=2010 International Conference on System Science and Engineering |chapter=Finding the right genes for disease and prognosis prediction |date=2010 |pages=1–2 |doi=10.1109/ICSSE.2010.5551766|isbn=978-1-4244-6472-2 |s2cid=21622726 }}</ref>

Genome-wide association studies have successfully identified thousands of common genetic variants for complex diseases and traits; however, these common variants only explain a small fraction of heritability.<ref>{{cite journal |last1=Manolio |first1=Teri A. |last2=Collins |first2=Francis S. |last3=Cox |first3=Nancy J. |last4=Goldstein |first4=David B. |last5=Hindorff |first5=Lucia A. |last6=Hunter |first6=David J. |last7=McCarthy |first7=Mark I. |last8=Ramos |first8=Erin M. |last9=Cardon |first9=Lon R. |last10=Chakravarti |first10=Aravinda |last11=Cho |first11=Judy H. |last12=Guttmacher |first12=Alan E. |last13=Kong |first13=Augustine |last14=Kruglyak |first14=Leonid |last15=Mardis |first15=Elaine |last16=Rotimi |first16=Charles N. |last17=Slatkin |first17=Montgomery |last18=Valle |first18=David |last19=Whittemore |first19=Alice S. |last20=Boehnke |first20=Michael |last21=Clark |first21=Andrew G. |last22=Eichler |first22=Evan E. |last23=Gibson |first23=Greg |last24=Haines |first24=Jonathan L. |last25=Mackay |first25=Trudy F. C. |last26=McCarroll |first26=Steven A. |last27=Visscher |first27=Peter M. |title=Finding the missing heritability of complex diseases |journal=Nature |date=October 2009 |volume=461 |issue=7265 |pages=747–753 |doi=10.1038/nature08494|pmid=19812666 |pmc=2831613 |bibcode=2009Natur.461..747M }}</ref> [[rare functional variant|Rare variants]] may account for some of the [[missing heritability]].<ref>{{cite journal |last1=Wainschtein |first1=Pierrick |last2=Jain |first2=Deepti |last3=Zheng |first3=Zhili |last4=Aslibekyan |first4=Stella |last5=Becker |first5=Diane |last6=Bi |first6=Wenjian |last7=Brody |first7=Jennifer |last8=Carlson |first8=Jenna C. |last9=Correa |first9=Adolfo |last10=Du |first10=Margaret Mengmeng |last11=Fernandez-Rhodes |first11=Lindsay |last12=Ferrier |first12=Kendra R. |last13=Graff |first13=Misa |last14=Guo |first14=Xiuqing |last15=He |first15=Jiang |last16=Heard-Costa |first16=Nancy L. |last17=Highland |first17=Heather M. |last18=Hirschhorn |first18=Joel N. |last19=Howard-Claudio |first19=Candace M. |last20=Isasi |first20=Carmen R. |last21=Jackson |first21=Rebecca |last22=Jiang |first22=Jicai |last23=Joehanes |first23=Roby |last24=Justice |first24=Anne E. |last25=Kalyani |first25=Rita R. |last26=Kardia |first26=Sharon |last27=Lange |first27=Ethan |last28=LeBoff |first28=Meryl |last29=Lee |first29=Seunggeun |last30=Li |first30=Xihao |last31=Li |first31=Zilin |last32=Lim |first32=Elise |last33=Lin |first33=Danyu |last34=Lin |first34=Xihong |last35=Liu |first35=Simin |last36=Lu |first36=Yingchang |last37=Manson |first37=JoAnn |last38=Martin |first38=Lisa |last39=McHugh |first39=Caitlin |last40=Mikulla |first40=Julie |last41=Musani |first41=Solomon K. |last42=Ng |first42=Maggie |last43=Nickerson |first43=Deborah |last44=Palmer |first44=Nicholette |last45=Perry |first45=James |last46=Peters |first46=Ulrike |last47=Preuss |first47=Michael |last48=Qi |first48=Qibin |last49=Raffield |first49=Laura |last50=Rasmussen-Torvik |first50=Laura |last51=Reiner |first51=Alex |last52=Russell |first52=Emily M. |last53=Sitlani |first53=Colleen |last54=Smith |first54=Jennifer |last55=Spracklen |first55=Cassandra N. |last56=Wang |first56=Tao |last57=Wang |first57=Zhe |last58=Wessel |first58=Jennifer |last59=Xu |first59=Hanfei |last60=Yaser |first60=Mohammad |last61=Yoneyama |first61=Sachiko |last62=Young |first62=Kendra A. |last63=Zhang |first63=Jingwen |last64=Zhang |first64=Xinruo |last65=Zhou |first65=Hufeng |last66=Zhu |first66=Xiaofeng |last67=Zoellner |first67=Sebastian |last68=Abe |first68=Namiko |last69=Abecasis |first69=Gonçalo |last70=Aguet |first70=Francois |last71=Almasy |first71=Laura |last72=Alonso |first72=Alvaro |last73=Ament |first73=Seth |last74=Anderson |first74=Peter |last75=Anugu |first75=Pramod |last76=Applebaum-Bowden |first76=Deborah |last77=Ardlie |first77=Kristin |last78=Arking |first78=Dan |last79=Ashley-Koch |first79=Allison |last80=Assimes |first80=Tim |last81=Auer |first81=Paul |last82=Avramopoulos |first82=Dimitrios |last83=Ayas |first83=Najib |last84=Balasubramanian |first84=Adithya |last85=Barnard |first85=John |last86=Barnes |first86=Kathleen |last87=Barr |first87=R. 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In [[cancer]], the genomes of affected cells are rearranged in complex or unpredictable ways. In addition to [[single-nucleotide polymorphism]] arrays identifying [[point mutation]]s that cause cancer, [[oligonucleotide]] microarrays can be used to identify chromosomal gains and losses (called [[comparative genomic hybridization]]). These detection methods generate [[terabyte]]s of data per experiment.<ref>Tsourakakis~~, C.~~ E., Tolliver~~, D.,~~ Tsiarli~~, M.~~ A., Shackney~~, S. E., &~~ Schwartz~~, R. (2010).~~ CGHTRIMMER: Discretizing noisy ~~array~~ CGH ~~data. arXiv. https://doi.org/10.48550/arxiv~~.1002.4438</ref> The data is often found to contain considerable variability, or [[noise]], and thus [[Hidden Markov model]] and change-point analysis methods are being developed to infer real [[copy number]] changes.<ref>Yau~~, C.,~~ Mouradov~~, D.,~~ Jorissen~~, R.~~ N., Colella~~, S.,~~ Mirza~~, G~~., Steers~~, G.,~~ Harris~~, A.,~~ Ragoussis~~, J.,~~ Sieber~~, O.~~ M., Holmes~~, C.~~ C.~~, & Silver, A. (2008).~~ A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data~~. PLoS Computational~~ Biology~~, 4(3), e1000052. https://~~doi~~.org/~~10.~~1371~~/~~journal.pcbi.0030052~~</ref>

In [[cancer]], the genomes of affected cells are rearranged in complex or unpredictable ways. In addition to [[single-nucleotide polymorphism]] arrays identifying [[point mutation]]s that cause cancer, [[oligonucleotide]] microarrays can be used to identify chromosomal gains and losses (called [[comparative genomic hybridization]]). These detection methods generate [[terabyte]]s of data per experiment.<ref>{{cite arXiv | last1=Tsourakakis | first1=Charalampos E. | last2=Tolliver | first2=David | last3=Tsiarli | first3=Maria A. | last4=Shackney | first4=Stanley | last5=Schwartz | first5=Russell | title=CGHTRIMMER: Discretizing noisy Array CGH Data | date=2010 | class=q-bio.GN | eprint=1002.4438 }}</ref> The data is often found to contain considerable variability, or [[noise]], and thus [[Hidden Markov model]] and change-point analysis methods are being developed to infer real [[copy number]] changes.<ref>{{cite journal

| last1 = Yau

| first1 = Christopher

| last2 = Mouradov

| first2 = Dmitri

| last3 = Jorissen

| first3 = Robert N.

| last4 = Colella

| first4 = Stefano

| last5 = Mirza

| first5 = Ghazala K.

| last6 = Steers

| first6 = Graham

| last7 = Harris

| first7 = Adrian

| last8 = Ragoussis

| first8 = Jiannis

| last9 = Sieber

| first9 = Oliver M.

| last10 = Holmes

| first10 = Chris C.

| title = A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data

| journal = Genome Biology

| year = 2010

| volume = 11

| number = 9

| pages = R92

| doi = 10.1186/gb-2010-11-9-r92

| pmid = 20804551

| doi-access = free

| pmc = 2941741

}}

</ref>

Two important principles can be used to identify cancer by mutations in the [[exome]]. First, cancer is a disease of accumulated somatic mutations in genes. Second, cancer contains driver mutations which need to be distinguished from passengers.<ref>{{cite journal | vauthors = Vazquez M, de la Torre V, Valencia A | title = Chapter 14: Cancer genome analysis | journal = PLOS Computational Biology | volume = 8 | issue = 12 | ~~pages~~ = e1002824 | date = 2012-12-27 | pmid = 23300415 | pmc = 3531315 | doi = 10.1371/journal.pcbi.1002824 | bibcode = 2012PLSCB...8E2824V | doi-access = free }}</ref>

Two important principles can be used to identify cancer by mutations in the [[exome]]. First, cancer is a disease of accumulated somatic mutations in genes. Second, cancer contains driver mutations which need to be distinguished from passengers.<ref>{{cite journal | vauthors = Vazquez M, de la Torre V, Valencia A | title = Chapter 14: Cancer genome analysis | journal = PLOS Computational Biology | volume = 8 | issue = 12 | article-number = e1002824 | date = 2012-12-27 | pmid = 23300415 | pmc = 3531315 | doi = 10.1371/journal.pcbi.1002824 | bibcode = 2012PLSCB...8E2824V | doi-access = free }}</ref>

Further improvements in bioinformatics could allow for classifying types of cancer by analysis of cancer driven mutations in the genome. Furthermore, tracking of patients while the disease progresses may be possible in the future with the sequence of cancer samples. Another type of data that requires novel informatics development is the analysis of [[lesion]]s found to be recurrent among many tumors.<ref>{{cite book | vauthors = Hye-Jung EC, Jaswinder K, Martin K, Samuel AA, Marco AM | veditors = Dellaire G, Berman JN, Arceci RJ | title= Cancer Genomics |date=2014 |publisher=Academic Press |location=Boston (US) |isbn=978-0-12-396967-5 |pages=13–30 |chapter=Second-Generation Sequencing for Cancer Genome Analysis |doi=10.1016/B978-0-12-396967-5.00002-5}}</ref>

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[[Gene regulation]] is a complex process where a signal, such as an extracellular signal such as a [[hormone]], eventually leads to an increase or decrease in the activity of one or more [[protein]]s. Bioinformatics techniques have been applied to explore various steps in this process.

For example, gene expression can be regulated by nearby elements in the genome. Promoter analysis involves the identification and study of [[sequence motif]]s in the DNA surrounding the protein-coding region of a gene. These motifs influence the extent to which that region is transcribed into mRNA. [[Enhancer (genetics)|Enhancer]] elements far away from the promoter can also regulate gene expression, through three-dimensional looping interactions. These interactions can be determined by bioinformatic analysis of [[chromosome conformation capture]] experiments.

For example, gene expression can be regulated by nearby elements in the genome. Promoter analysis involves the identification and study of [[sequence motif]]s in the DNA surrounding the [[protein-coding region]] of a gene. These motifs influence the extent to which that region is transcribed into mRNA. [[Enhancer (genetics)|Enhancer]] elements far away from the promoter can also regulate gene expression, through three-dimensional looping interactions. These interactions can be determined by bioinformatic analysis of [[chromosome conformation capture]] experiments.

Expression data can be used to infer gene regulation: one might compare [[microarray]] data from a wide variety of states of an organism to form hypotheses about the genes involved in each state. In a single-cell organism, one might compare stages of the [[cell cycle]], along with various stress conditions (heat shock, starvation, etc.). [[cluster analysis|Clustering algorithms]] can be then applied to expression data to determine which genes are co-expressed. For example, the upstream regions (promoters) of co-expressed genes can be searched for over-represented [[regulatory elements]]. Examples of clustering algorithms applied in gene clustering are [[k-means clustering]], [[self-organizing map]]s (SOMs), [[hierarchical clustering]], and [[consensus clustering]] methods.

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===Protein localization===

Finding the location of proteins allows us to predict what they do. This is called [[protein function prediction]]. For instance, if a protein is found in the [[Cell nucleus|nucleus]] it may be involved in [[gene regulation]] or [[RNA splicing|splicing]]. By contrast, if a protein is found in [[mitochondria]], it may be involved in [[Cellular respiration|respiration]] or other [[Metabolism|metabolic processes]]. There are well developed [[protein subcellular localization prediction]] resources available, including protein subcellular location databases, and prediction tools.<ref>{{Cite web |url=https://www.proteinatlas.org/humancell |title=The human cell |website=www.proteinatlas.org |access-date=2017-10-02 |archive-date=2 October 2017 |archive-url=https://web.archive.org/web/20171002215345/https://www.proteinatlas.org/humancell |url-status=live }}</ref><ref>{{cite journal | vauthors = Thul PJ, Åkesson L, Wiking M, Mahdessian D, Geladaki A, Ait Blal H, Alm T, Asplund A, Björk L, Breckels LM, Bäckström A, Danielsson F, Fagerberg L, Fall J, Gatto L, Gnann C, Hober S, Hjelmare M, Johansson F, Lee S, Lindskog C, Mulder J, Mulvey CM, Nilsson P, Oksvold P, Rockberg J, Schutten R, Schwenk JM, Sivertsson Å, Sjöstedt E, Skogs M, Stadler C, Sullivan DP, Tegel H, Winsnes C, Zhang C, Zwahlen M, Mardinoglu A, Pontén F, von Feilitzen K, Lilley KS, Uhlén M, Lundberg E | title = A subcellular map of the human proteome | journal = Science | volume = 356 | issue = 6340 | ~~pages~~ = eaal3321 | date = May 2017 | pmid = 28495876 | doi = 10.1126/science.aal3321 | s2cid = 10744558 }}</ref>

Finding the location of proteins allows us to predict what they do. This is called [[protein function prediction]]. For instance, if a protein is found in the [[Cell nucleus|nucleus]] it may be involved in [[gene regulation]] or [[RNA splicing|splicing]]. By contrast, if a protein is found in [[mitochondria]], it may be involved in [[Cellular respiration|respiration]] or other [[Metabolism|metabolic processes]]. There are well developed [[protein subcellular localization prediction]] resources available, including protein subcellular location databases, and prediction tools.<ref>{{Cite web |url=https://www.proteinatlas.org/humancell |title=The human cell |website=www.proteinatlas.org |access-date=2017-10-02 |archive-date=2 October 2017 |archive-url=https://web.archive.org/web/20171002215345/https://www.proteinatlas.org/humancell |url-status=live }}</ref><ref>{{cite journal | vauthors = Thul PJ, Åkesson L, Wiking M, Mahdessian D, Geladaki A, Ait Blal H, Alm T, Asplund A, Björk L, Breckels LM, Bäckström A, Danielsson F, Fagerberg L, Fall J, Gatto L, Gnann C, Hober S, Hjelmare M, Johansson F, Lee S, Lindskog C, Mulder J, Mulvey CM, Nilsson P, Oksvold P, Rockberg J, Schutten R, Schwenk JM, Sivertsson Å, Sjöstedt E, Skogs M, Stadler C, Sullivan DP, Tegel H, Winsnes C, Zhang C, Zwahlen M, Mardinoglu A, Pontén F, von Feilitzen K, Lilley KS, Uhlén M, Lundberg E | title = A subcellular map of the human proteome | journal = Science | volume = 356 | issue = 6340 | article-number = eaal3321 | date = May 2017 | pmid = 28495876 | doi = 10.1126/science.aal3321 | bibcode = 2017Sci...356l3321T | s2cid = 10744558 }}</ref>

===Nuclear organization of chromatin===

Data from high-throughput [[chromosome conformation capture]] experiments, such as [[Hi-C (experiment)]] and [[ChIA-PET]], can provide information on the three-dimensional structure and [[nuclear organization]] of [[chromatin]]. Bioinformatic challenges in this field include partitioning the genome into domains, such as [[Topologically Associating Domain]]s (TADs), that are organised together in three-dimensional space.<ref>{{cite journal | vauthors = Ay F, Noble WS | title = Analysis methods for studying the 3D architecture of the genome | journal = Genome Biology | volume = 16 | issue = 1 | ~~pages~~ = 183 | date = September 2015 | pmid = 26328929 | pmc = 4556012 | doi = 10.1186/s13059-015-0745-7 | doi-access = free }}</ref>

Data from high-throughput [[chromosome conformation capture]] experiments, such as [[Hi-C (experiment)]] and [[ChIA-PET]], can provide information on the three-dimensional structure and [[nuclear organization]] of [[chromatin]]. Bioinformatic challenges in this field include partitioning the genome into domains, such as [[Topologically Associating Domain]]s (TADs), that are organised together in three-dimensional space.<ref>{{cite journal | vauthors = Ay F, Noble WS | title = Analysis methods for studying the 3D architecture of the genome | journal = Genome Biology | volume = 16 | issue = 1 | article-number = 183 | date = September 2015 | pmid = 26328929 | pmc = 4556012 | doi = 10.1186/s13059-015-0745-7 | doi-access = free }}</ref>

==Structural bioinformatics==

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Finding the structure of proteins is an important application of bioinformatics. The Critical Assessment of Protein Structure Prediction (CASP) is an open competition where worldwide research groups submit protein models for evaluating unknown protein models.<ref>{{Cite journal|title=Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XIII|year=2019 |pmc=6927249 |last1=Kryshtafovych |first1=A. |last2=Schwede |first2=T. |last3=Topf |first3=M. |last4=Fidelis |first4=K. |last5=Moult |first5=J. |journal=Proteins |volume=87 |issue=12 |pages=1011–1020 |doi=10.1002/prot.25823 |pmid=31589781 }}</ref><ref>{{Cite web |title=Home - CASP14 |url=https://predictioncenter.org/casp14/ |access-date=2023-06-12 |website=predictioncenter.org |archive-date=30 January 2023 |archive-url=https://web.archive.org/web/20230130200222/https://predictioncenter.org/casp14/ |url-status=live }}</ref>

=== Amino acid sequence ===

===Amino acid sequence===

The linear [[amino acid]] sequence of a protein is called the [[primary structure]]. The primary structure can be easily determined from the sequence of [[codons]] on the DNA gene that codes for it. In most proteins, the primary structure uniquely determines the 3-dimensional structure of a protein in its native environment. An exception is the misfolded [[prion]] protein involved in [[bovine spongiform encephalopathy]]. This structure is linked to the function of the protein. Additional structural information includes the ''[[secondary structure|secondary]]'', ''[[tertiary structure|tertiary]]'' and ''[[quaternary structure|quaternary]]'' structure. A viable general solution to the prediction of the function of a protein remains an open problem. Most efforts have so far been directed towards heuristics that work most of the time.{{citation needed|date=July 2015}}

=== Homology ===

===Homology===

In the genomic branch of bioinformatics, homology is used to predict the function of a gene: if the sequence of gene ''A'', whose function is known, is homologous to the sequence of gene ''B,'' whose function is unknown, one could infer that B may share A's function. In structural bioinformatics, homology is used to determine which parts of a protein are important in structure formation and interaction with other proteins. [[Homology modeling]] is used to predict the structure of an unknown protein from existing homologous proteins.

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===Molecular interaction networks===

[[File:The protein interaction network of Treponema pallidum.png|200px|thumbnail|right|Interactions between proteins are frequently visualized and analyzed using networks. This network is made up of protein–protein interactions from ''[[Treponema pallidum]]'', the causative agent of [[syphilis]] and other diseases.<ref>{{cite journal | vauthors = Titz B, Rajagopala SV, Goll J, Häuser R, McKevitt MT, Palzkill T, Uetz P | title = The binary protein interactome of Treponema pallidum--the syphilis spirochete | journal = PLOS ONE | volume = 3 | issue = 5 | ~~pages~~ = e2292 | date = May 2008 | pmid = 18509523 | pmc = 2386257 | doi = 10.1371/journal.pone.0002292 | bibcode = 2008PLoSO...3.2292T | veditors = Hall N | doi-access = free }}</ref>]]

[[File:The protein interaction network of Treponema pallidum.png|200px|thumbnail|right|Interactions between proteins are frequently visualized and analyzed using networks. This network is made up of protein–protein interactions from ''[[Treponema pallidum]]'', the causative agent of [[syphilis]] and other diseases.<ref>{{cite journal | vauthors = Titz B, Rajagopala SV, Goll J, Häuser R, McKevitt MT, Palzkill T, Uetz P | title = The binary protein interactome of Treponema pallidum--the syphilis spirochete | journal = PLOS ONE | volume = 3 | issue = 5 | article-number = e2292 | date = May 2008 | pmid = 18509523 | pmc = 2386257 | doi = 10.1371/journal.pone.0002292 | bibcode = 2008PLoSO...3.2292T | veditors = Hall N | doi-access = free }}</ref>]]

Tens of thousands of three-dimensional protein structures have been determined by [[X-ray crystallography]] and [[protein nuclear magnetic resonance spectroscopy]] (protein NMR) and a central question in structural bioinformatics is whether it is practical to predict possible protein–protein interactions only based on these 3D shapes, without performing [[protein–protein interaction]] experiments. A variety of methods have been developed to tackle the [[protein–protein docking]] problem, though it seems that there is still much work to be done in this field.

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===Ontologies and data integration===

Biological ontologies are [[directed acyclic graph]]s of [[controlled vocabularies]]. They create categories for biological concepts and descriptions so they can be easily analyzed with computers. When categorised in this way, it is possible to gain added value from holistic and integrated analysis.{{~~Citation needed~~|date=~~June 2023~~}}

Biological ontologies are [[directed acyclic graph]]s of [[controlled vocabularies]]. They create categories for biological concepts and descriptions so they can be easily analyzed with computers. When categorised in this way, it is possible to gain added value from holistic and integrated analysis.<ref>{{Cite journal |last=Groß |first=Anika |last2=Pruski |first2=Cédric |last3=Rahm |first3=Erhard |date=2016-01-01 |title=Evolution of biomedical ontologies and mappings: Overview of recent approaches |url=https://www.sciencedirect.com/science/article/pii/S2001037016300319 |journal=Computational and Structural Biotechnology Journal |volume=14 |pages=333–340 |doi=10.1016/j.csbj.2016.08.002 |issn=2001-0370|doi-access=free |pmc=5018063 }}</ref>

The [[OBO Foundry]] was an effort to standardise certain ontologies. One of the most widespread is the [[Gene ontology]] which describes gene function. There are also ontologies which describe phenotypes.

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Basic bioinformatics services are classified by the [[European Bioinformatics Institute|EBI]] into three categories: [[Sequence alignment software|SSS]] (Sequence Search Services), [[Multiple sequence alignment|MSA]] (Multiple Sequence Alignment), and [[#Sequence analysis|BSA]] (Biological Sequence Analysis).<ref>{{Cite book | vauthors = Nisbet R, Elder IV J, Miner G |title=Handbook of Statistical Analysis and Data Mining Applications |chapter-url=https://books.google.com/books?id=U5np34a5fmQC&q=bioinformatics%20service%20categories%20EBI&pg=PA328|publisher=Academic Press |year=2009 |page=328 |chapter=Bioinformatics |isbn=978-0-08-091203-5 }}</ref> The availability of these [[Service-orientation|service-oriented]] bioinformatics resources demonstrate the applicability of web-based bioinformatics solutions, and range from a collection of standalone tools with a common data format under a single web-based interface, to integrative, distributed and extensible [[bioinformatics workflow management systems]].

==== Bioinformatics workflow management systems ====

====Bioinformatics workflow management systems====

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=== BioCompute and BioCompute Objects ===

===BioCompute and BioCompute Objects===

In 2014, the [[US Food and Drug Administration]] sponsored a conference held at the [[National Institutes of Health]] Bethesda Campus to discuss reproducibility in bioinformatics.<ref>{{Cite web|url=https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm389561.htm|title=Advancing Regulatory Science – Sept. 24–25, 2014 Public Workshop: Next Generation Sequencing Standards|author=Office of the Commissioner|website=www.fda.gov|language=en|access-date=2017-11-30|archive-date=14 November 2017|archive-url=https://web.archive.org/web/20171114200347/https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm389561.htm~~|url-status=dead~~}}</ref> Over the next three years, a consortium of stakeholders met regularly to discuss what would become BioCompute paradigm.<ref>{{cite journal | vauthors = Simonyan V, Goecks J, Mazumder R | title = Biocompute Objects-A Step towards Evaluation and Validation of Biomedical Scientific Computations | journal = PDA Journal of Pharmaceutical Science and Technology | volume = 71 | issue = 2 | pages = 136–146 | date = 2017 | pmid = 27974626 | pmc = 5510742 | doi = 10.5731/pdajpst.2016.006734 }}</ref> These stakeholders included representatives from government, industry, and academic entities. Session leaders represented numerous branches of the FDA and NIH Institutes and Centers, non-profit entities including the [[Human Variome Project]] and the [[European Federation for Medical Informatics]], and research institutions including [[Stanford]], the [[New York Genome Center]], and the [[George Washington University]].

In 2014, the [[US Food and Drug Administration]] sponsored a conference held at the [[National Institutes of Health]] Bethesda Campus to discuss reproducibility in bioinformatics.<ref>{{Cite web|url=https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm389561.htm|title=Advancing Regulatory Science – Sept. 24–25, 2014 Public Workshop: Next Generation Sequencing Standards|author=Office of the Commissioner|website=www.fda.gov|language=en|access-date=2017-11-30|archive-date=14 November 2017|archive-url=https://web.archive.org/web/20171114200347/https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm389561.htm}}</ref> Over the next three years, a consortium of stakeholders met regularly to discuss what would become BioCompute paradigm.<ref>{{cite journal | vauthors = Simonyan V, Goecks J, Mazumder R | title = Biocompute Objects-A Step towards Evaluation and Validation of Biomedical Scientific Computations | journal = PDA Journal of Pharmaceutical Science and Technology | volume = 71 | issue = 2 | pages = 136–146 | date = 2017 | pmid = 27974626 | pmc = 5510742 | doi = 10.5731/pdajpst.2016.006734 }}</ref> These stakeholders included representatives from government, industry, and academic entities. Session leaders represented numerous branches of the FDA and NIH Institutes and Centers, non-profit entities including the [[Human Variome Project]] and the [[European Federation for Medical Informatics]], and research institutions including [[Stanford]], the [[New York Genome Center]], and the [[George Washington University]].

It was decided that the BioCompute paradigm would be in the form of digital 'lab notebooks' which allow for the reproducibility, replication, review, and reuse, of bioinformatics protocols. This was proposed to enable greater continuity within a research group over the course of normal personnel flux while furthering the exchange of ideas between groups. The US FDA funded this work so that information on pipelines would be more transparent and accessible to their regulatory staff.<ref>{{Cite web|url=https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm491893.htm|title=Advancing Regulatory Science – Community-based development of HTS standards for validating data and computation and encouraging interoperability|author=Office of the Commissioner|website=www.fda.gov|language=en|access-date=2017-11-30|archive-date=26 January 2018|archive-url=https://web.archive.org/web/20180126133504/https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm491893.htm~~|url-status=dead~~}}</ref>

It was decided that the BioCompute paradigm would be in the form of digital 'lab notebooks' which allow for the reproducibility, replication, review, and reuse, of bioinformatics protocols. This was proposed to enable greater continuity within a research group over the course of normal personnel flux while furthering the exchange of ideas between groups. The US FDA funded this work so that information on pipelines would be more transparent and accessible to their regulatory staff.<ref>{{Cite web|url=https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm491893.htm|title=Advancing Regulatory Science – Community-based development of HTS standards for validating data and computation and encouraging interoperability|author=Office of the Commissioner|website=www.fda.gov|language=en|access-date=2017-11-30|archive-date=26 January 2018|archive-url=https://web.archive.org/web/20180126133504/https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm491893.htm}}</ref>

In 2016, the group reconvened at the NIH in Bethesda and discussed the potential for a [[BioCompute Object]], an instance of the BioCompute paradigm. This work was copied as both a "standard trial use" document and a preprint paper uploaded to bioRxiv. The BioCompute object allows for the JSON-ized record to be shared among employees, collaborators, and regulators.<ref>{{cite journal | vauthors = Alterovitz G, Dean D, Goble C, Crusoe MR, Soiland-Reyes S, Bell A, Hayes A, Suresh A, Purkayastha A, King CH, Taylor D, Johanson E, Thompson EE, Donaldson E, Morizono H, Tsang H, Vora JK, Goecks J, Yao J, Almeida JS, Keeney J, Addepalli K, Krampis K, Smith KM, Guo L, Walderhaug M, Schito M, Ezewudo M, Guimera N, Walsh P, Kahsay R, Gottipati S, Rodwell TC, Bloom T, Lai Y, Simonyan V, Mazumder R | title = Enabling precision medicine via standard communication of HTS provenance, analysis, and results | journal = PLOS Biology | volume = 16 | issue = 12 | ~~pages~~ = e3000099 | date = December 2018 | pmid = 30596645 | doi = 10.1371/journal.pbio.3000099 | pmc = 6338479 | doi-access = free }}</ref><ref>{{Citation|title=BioCompute Object (BCO) project is a collaborative and community-driven framework to standardize HTS computational data. 1. BCO Specification Document: user manual for understanding and creating B.|date=2017-09-03|url=https://github.com/biocompute-objects/HTS-CSRS|publisher=biocompute-objects|access-date=30 November 2017|archive-date=27 June 2018|archive-url=https://web.archive.org/web/20180627081221/https://github.com/biocompute-objects/HTS-CSRS|url-status=live}}</ref>

In 2016, the group reconvened at the NIH in Bethesda and discussed the potential for a [[BioCompute Object]], an instance of the BioCompute paradigm. This work was copied as both a "standard trial use" document and a preprint paper uploaded to bioRxiv. The BioCompute object allows for the JSON-ized record to be shared among employees, collaborators, and regulators.<ref>{{cite journal | vauthors = Alterovitz G, Dean D, Goble C, Crusoe MR, Soiland-Reyes S, Bell A, Hayes A, Suresh A, Purkayastha A, King CH, Taylor D, Johanson E, Thompson EE, Donaldson E, Morizono H, Tsang H, Vora JK, Goecks J, Yao J, Almeida JS, Keeney J, Addepalli K, Krampis K, Smith KM, Guo L, Walderhaug M, Schito M, Ezewudo M, Guimera N, Walsh P, Kahsay R, Gottipati S, Rodwell TC, Bloom T, Lai Y, Simonyan V, Mazumder R | title = Enabling precision medicine via standard communication of HTS provenance, analysis, and results | journal = PLOS Biology | volume = 16 | issue = 12 | article-number = e3000099 | date = December 2018 | pmid = 30596645 | doi = 10.1371/journal.pbio.3000099 | pmc = 6338479 | doi-access = free }}</ref><ref>{{Citation|title=BioCompute Object (BCO) project is a collaborative and community-driven framework to standardize HTS computational data. 1. BCO Specification Document: user manual for understanding and creating B.|date=2017-09-03|url=https://github.com/biocompute-objects/HTS-CSRS|publisher=biocompute-objects|access-date=30 November 2017|archive-date=27 June 2018|archive-url=https://web.archive.org/web/20180627081221/https://github.com/biocompute-objects/HTS-CSRS|url-status=live}}</ref>

==Education platforms==

While bioinformatics is taught as an in-person [[master's degree]] at many universities, there are many other methods and technologies available to learn and obtain certification in the subject. The computational nature of bioinformatics lends it to [[Educational technology|computer-aided and online learning]].<ref>{{Cite journal |last=Campbell |first=A. Malcolm |date=2003-06-01 |title=Public Access for Teaching Genomics, Proteomics, and Bioinformatics |journal=Cell Biology Education |volume=2 |issue=2 |pages=98–111 |doi=10.1187/cbe.03-02-0007 |pmc=162192 |pmid=12888845}}</ref><ref>{{Cite journal |last=Arenas |first=Miguel |date=September 2021 |title=General considerations for online teaching practices in bioinformatics in the time of COVID -19 |journal=Biochemistry and Molecular Biology Education |language=en |volume=49 |issue=5 |pages=683–684 |doi=10.1002/bmb.21558 |issn=1470-8175 |pmc=8426940 |pmid=34231941}}</ref> Software platforms designed to teach bioinformatics concepts and methods include [[Rosalind (education platform)|Rosalind]] and online courses offered through the [[Swiss Institute of Bioinformatics]] Training Portal. The [[Canadian Bioinformatics Workshops]] provides videos and slides from training workshops on their website under a [[Creative Commons]] license. The 4273π project or 4273pi project<ref>{{cite journal | vauthors = Barker D, Ferrier DE, Holland PW, Mitchell JB, Plaisier H, Ritchie MG, Smart SD | title = 4273π: bioinformatics education on low cost ARM hardware | journal = BMC Bioinformatics | volume = 13 | ~~pages~~ = 522 | date = August 2013 | pmid = 23937194 | pmc = 3751261 | doi = 10.1186/1471-2105-14-243 | doi-access = free }}</ref> also offers open source educational materials for free. The course runs on low cost [[Raspberry Pi]] computers and has been used to teach adults and school pupils.<ref>{{cite journal | vauthors = Barker D, Alderson RG, McDonagh JL, Plaisier H, Comrie MM, Duncan L, Muirhead GT, Sweeney SD |title=University-level practical activities in bioinformatics benefit voluntary groups of pupils in the last 2 years of school |journal=International Journal of STEM Education |date=2015 |volume=2 |issue=17 |doi=10.1186/s40594-015-0030-z | s2cid = 256396656 |hdl=10023/7704 |hdl-access=free | doi-access = free }}</ref><ref>{{cite journal | vauthors = McDonagh JL, Barker D, Alderson RG | title = Bringing computational science to the public | journal = SpringerPlus | volume = 5 | issue = 259 | ~~pages~~ = 259 | date = 2016 | pmid = 27006868 | pmc = 4775721 | doi = 10.1186/s40064-016-1856-7 | doi-access = free }}</ref> 4273 is actively developed by a consortium of academics and research staff who have run research level bioinformatics using Raspberry Pi computers and the 4273π operating system.<ref>{{cite journal | vauthors = Robson JF, Barker D | title = Comparison of the protein-coding gene content of Chlamydia trachomatis and Protochlamydia amoebophila using a Raspberry Pi computer | journal = BMC Research Notes | volume = 8 | issue = 561 | ~~pages~~ = 561 | date = October 2015 | pmid = 26462790 | pmc = 4604092 | doi = 10.1186/s13104-015-1476-2 | doi-access = free }}</ref><ref>{{cite journal | vauthors = Wreggelsworth KM, Barker D | title = A comparison of the protein-coding genomes of two green sulphur bacteria, Chlorobium tepidum TLS and Pelodictyon phaeoclathratiforme BU-1 | journal = BMC Research Notes | volume = 8 | issue = 565 | ~~pages~~ = 565 | date = October 2015 | pmid = 26467441 | pmc = 4606965 | doi = 10.1186/s13104-015-1535-8 | doi-access = free }}</ref>

While bioinformatics is taught as an in-person [[master's degree]] at many universities, there are many other methods and technologies available to learn and obtain certification in the subject. The computational nature of bioinformatics lends it to [[Educational technology|computer-aided and online learning]].<ref>{{Cite journal |last=Campbell |first=A. Malcolm |date=2003-06-01 |title=Public Access for Teaching Genomics, Proteomics, and Bioinformatics |journal=Cell Biology Education |volume=2 |issue=2 |pages=98–111 |doi=10.1187/cbe.03-02-0007 |pmc=162192 |pmid=12888845}}</ref><ref>{{Cite journal |last=Arenas |first=Miguel |date=September 2021 |title=General considerations for online teaching practices in bioinformatics in the time of COVID -19 |journal=Biochemistry and Molecular Biology Education |language=en |volume=49 |issue=5 |pages=683–684 |doi=10.1002/bmb.21558 |issn=1470-8175 |pmc=8426940 |pmid=34231941}}</ref> Software platforms designed to teach bioinformatics concepts and methods include [[Rosalind (education platform)|Rosalind]] and online courses offered through the [[Swiss Institute of Bioinformatics]] Training Portal. The [[Canadian Bioinformatics Workshops]] provides videos and slides from training workshops on their website under a [[Creative Commons]] license. The 4273π project or 4273pi project<ref>{{cite journal | vauthors = Barker D, Ferrier DE, Holland PW, Mitchell JB, Plaisier H, Ritchie MG, Smart SD | title = 4273π: bioinformatics education on low cost ARM hardware | journal = BMC Bioinformatics | volume = 13 | page = 522 | date = August 2013 | article-number = 243 | pmid = 23937194 | pmc = 3751261 | doi = 10.1186/1471-2105-14-243 | doi-access = free }}</ref> also offers open source educational materials for free. The course runs on low cost [[Raspberry Pi]] computers and has been used to teach adults and school pupils.<ref>{{cite journal | vauthors = Barker D, Alderson RG, McDonagh JL, Plaisier H, Comrie MM, Duncan L, Muirhead GT, Sweeney SD |title=University-level practical activities in bioinformatics benefit voluntary groups of pupils in the last 2 years of school |journal=International Journal of STEM Education |date=2015 |volume=2 |issue=17 |article-number=17 |doi=10.1186/s40594-015-0030-z | s2cid = 256396656 |hdl=10023/7704 |hdl-access=free | doi-access = free }}</ref><ref>{{cite journal | vauthors = McDonagh JL, Barker D, Alderson RG | title = Bringing computational science to the public | journal = SpringerPlus | volume = 5 | issue = 259 | article-number = 259 | date = 2016 | pmid = 27006868 | pmc = 4775721 | doi = 10.1186/s40064-016-1856-7 | doi-access = free }}</ref> 4273 is actively developed by a consortium of academics and research staff who have run research level bioinformatics using Raspberry Pi computers and the 4273π operating system.<ref>{{cite journal | vauthors = Robson JF, Barker D | title = Comparison of the protein-coding gene content of Chlamydia trachomatis and Protochlamydia amoebophila using a Raspberry Pi computer | journal = BMC Research Notes | volume = 8 | issue = 561 | article-number = 561 | date = October 2015 | pmid = 26462790 | pmc = 4604092 | doi = 10.1186/s13104-015-1476-2 | doi-access = free }}</ref><ref>{{cite journal | vauthors = Wreggelsworth KM, Barker D | title = A comparison of the protein-coding genomes of two green sulphur bacteria, Chlorobium tepidum TLS and Pelodictyon phaeoclathratiforme BU-1 | journal = BMC Research Notes | volume = 8 | issue = 565 | article-number = 565 | date = October 2015 | pmid = 26467441 | pmc = 4606965 | doi = 10.1186/s13104-015-1535-8 | doi-access = free }}</ref>

[[MOOC]] platforms also provide online certifications in bioinformatics and related disciplines, including [[Coursera]]'s Bioinformatics Specialization at the [[University of California, San Diego]], Genomic Data Science Specialization at [[Johns Hopkins University]], and [[EdX]]'s Data Analysis for Life Sciences XSeries at [[Harvard University]].

==Conferences==

There are several large conferences that are concerned with bioinformatics. Some of the most notable examples are [[Intelligent Systems for Molecular Biology]] (ISMB), [~~[European Conference~~ on ~~Computational Biology]~~] (~~ECCB~~), and [[Research in Computational Molecular Biology]] (RECOMB).

There are several large conferences that are concerned with bioinformatics. Some of the most notable examples are [[European Conference on Computational Biology]] (ECCB), [[Intelligent Systems for Molecular Biology]] (ISMB), [https://psb.stanford.edu Pacific Symposium on Biocomputing] (PSB), and [[Research in Computational Molecular Biology]] (RECOMB).

== See also ==

==See also==

{{Columns-list|colwidth=30em|

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}}

== References ==

==References==

== Further reading ==

==Further reading==

* Sehgal et al. : Structural, phylogenetic and docking studies of D-amino acid oxidase activator(DAOA ), a candidate schizophrenia gene. Theoretical Biology and Medical Modelling 2013 10 :3.

* Sehgal et al. : Structural, phylogenetic and docking studies of D-amino acid oxidase activator (DAOA), a candidate schizophrenia gene. Theoretical Biology and Medical Modelling 2013 10 :3.

* Achuthsankar S Nair [http://print.achuth.googlepages.com/BINFTutorialV5.0CSI07.pdf Computational Biology & Bioinformatics – A gentle Overview] {{Webarchive|url=https://web.archive.org/web/20081216212125/http://print.achuth.googlepages.com/BINFTutorialV5.0CSI07.pdf |date=16 December 2008 }}, Communications of Computer Society of India, January 2007

* [[Srinivas Aluru|Aluru, Srinivas]], ed. ''Handbook of Computational Molecular Biology''. Chapman & Hall/Crc, 2006. {{ISBN|1-58488-406-1}} (Chapman & Hall/Crc Computer and Information Science Series)

* Baldi, P and Brunak, S, ''Bioinformatics: The Machine Learning Approach'', 2nd edition. MIT Press, 2001. {{ISBN|0-262-02506-X}}

* Baldi, P and [[Søren Brunak|Brunak]], S, ''Bioinformatics: The Machine Learning Approach'', 2nd edition. MIT Press, 2001. {{ISBN|0-262-02506-X}}

* Barnes, M.R. and Gray, I.C., eds., ''Bioinformatics for Geneticists'', first edition. Wiley, 2003. {{ISBN|0-470-84394-2}}

* Baxevanis, A.D. and Ouellette, B.F.F., eds., ''Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins'', third edition. Wiley, 2005. {{ISBN|0-471-47878-4}}

* Baxevanis, A.D. and [[Francis Ouellette|Ouellette]], B.F.F., eds., ''Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins'', third edition. Wiley, 2005. {{ISBN|0-471-47878-4}}

* Baxevanis, A.D., Petsko, G.A., Stein, L.D., and Stormo, G.D., eds., ''[[Current Protocols]] in Bioinformatics''. Wiley, 2007. {{ISBN|0-471-25093-7}}

* Cristianini, N. and Hahn, M. [http://www.computational-genomics.net/ ''Introduction to Computational Genomics''] {{Webarchive|url=https://web.archive.org/web/20090104042023/http://www.computational-genomics.net/ |date=4 January 2009 }}, Cambridge University Press, 2006. ({{ISBN|9780521671910}} |{{ISBN|0-521-67191-4}})

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* Stevens, Hallam, ''Life Out of Sequence: A Data-Driven History of Bioinformatics'', Chicago: The University of Chicago Press, 2013, {{ISBN|9780226080208}}

* Tisdall, James. "Beginning Perl for Bioinformatics" O'Reilly, 2001. {{ISBN|0-596-00080-4}}

* [~~http~~://www.nap.edu/catalog/11480.html Catalyzing Inquiry at the Interface of Computing and Biology (2005) CSTB report] {{Webarchive|url=https://web.archive.org/web/20070128222920/http://www.nap.edu/catalog/11480.html |date=28 January 2007 }}

* [https://www.nap.edu/catalog/11480.html Catalyzing Inquiry at the Interface of Computing and Biology (2005) CSTB report] {{Webarchive|url=https://web.archive.org/web/20070128222920/http://www.nap.edu/catalog/11480.html |date=28 January 2007 }}

* [~~http~~://www.nap.edu/catalog/2121.html Calculating the Secrets of Life: Contributions of the Mathematical Sciences and computing to Molecular Biology (1995)] {{Webarchive|url=https://web.archive.org/web/20080706035211/http://www.nap.edu/catalog/2121.html |date=6 July 2008 }}

* [https://www.nap.edu/catalog/2121.html Calculating the Secrets of Life: Contributions of the Mathematical Sciences and computing to Molecular Biology (1995)] {{Webarchive|url=https://web.archive.org/web/20080706035211/http://www.nap.edu/catalog/2121.html |date=6 July 2008 }}

* [https://web.archive.org/web/20071222091912/http://ocw.mit.edu/OcwWeb/Biology/7-91JSpring2004/LectureNotes/index.htm Foundations of Computational and Systems Biology MIT Course]

* [~~http~~://compbio.mit.edu/6.047/ Computational Biology: Genomes, Networks, Evolution Free MIT Course] {{Webarchive|url=https://web.archive.org/web/20130408034631/http://compbio.mit.edu/6.047/ |date=8 April 2013 }}

* [https://compbio.mit.edu/6.047/ Computational Biology: Genomes, Networks, Evolution Free MIT Course] {{Webarchive|url=https://web.archive.org/web/20130408034631/http://compbio.mit.edu/6.047/ |date=8 April 2013 }}

== External links ==

==External links==

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* [~~http~~://expasy.org Bioinformatics Resource Portal (SIB)]

* [https://expasy.org Bioinformatics Resource Portal (SIB)]

@@ Line 6: / Line 6: @@
 [[File:WPP domain alignment.PNG|500px|thumbnail|right|Early bioinformatics—computational alignment of experimentally determined sequences of a class of related proteins; see {{Section link||Sequence analysis}} for further information.]]
 [[Image:Genome viewer screenshot small.png|thumbnail|220px|Map of the human X chromosome (from the [[National Center for Biotechnology Information]] (NCBI) website)]]
+'''Bioinformatics''' ({{IPAc-en|audio=en-us-bioinformatics.ogg|ˌ|b|aɪ|.|oʊ|ˌ|ɪ|n|f|ɚ|ˈ|m|æ|t|ɪ|k|s}}) is an [[interdisciplinary]] field of [[science]] that develops computational methods and [[Bioinformatics software|software tool]]s for understanding [[biological]] data, especially when the data sets are large and complex. Bioinformatics integrates principles from [[biology]], [[chemistry]], [[physics]], [[computer science]], [[data science]], [[computer programming]], [[information engineering]], [[mathematics]], and [[statistics]] to analyze and interpret [[biological data]].<ref name=":1" /> This process can sometimes be referred to as [[computational biology]]; however, the distinction between the two terms is often disputed. The term ''[[computational biology]]'' can refer to building and using models of [[Biological system|biological systems]].
-'''Bioinformatics''' ({{IPAc-en|audio=en-us-bioinformatics.ogg|ˌ|b|aɪ|.|oʊ|ˌ|ɪ|n|f|ɚ|ˈ|m|æ|t|ɪ|k|s}}) is an [[interdisciplinary]] field of [[science]] that develops methods and [[Bioinformatics software|software tool]]s for understanding [[biological]] data, especially when the data sets are large and complex. Bioinformatics uses [[biology]], [[chemistry]], [[physics]], [[computer science]], [[data science]], [[computer programming]], [[information engineering]], [[mathematics]] and [[statistics]] to analyze and interpret [[biological data]]. This process  can sometimes be referred to as [[computational biology]], however the distinction between the two terms is often disputed. To some, the term ''computational biology'' refers to building and using models of biological systems.
 Computational, statistical, and computer programming techniques have been used  for [[In silico|computer simulation]] analyses of biological queries. They include reused specific analysis "pipelines", particularly in the field of [[genomics]], such as by the identification of [[gene]]s and single [[nucleotide]] polymorphisms ([[SNPs]]). These pipelines are used to better understand the genetic basis of disease, unique adaptations, desirable properties (especially in agricultural species), or differences between populations. Bioinformatics also includes [[proteomics]], which aims  to understand the organizational principles within [[nucleic acid]] and [[protein]] sequences.<ref>{{cite web |vauthors=Lesk AM |date=26 July 2013 |title=Bioinformatics |url=https://www.britannica.com/science/bioinformatics |website=Encyclopaedia Britannica |access-date=17 April 2017 |archive-date=14 April 2021 |archive-url=https://web.archive.org/web/20210414103621/https://www.britannica.com/science/bioinformatics |url-status=live }}</ref>
-Image and [[signal processing]] allow extraction of useful results from large amounts of raw data. It aids in sequencing and annotating genomes and their observed [[mutation]]s. Bioinformatics includes [[text mining]] of biological literature and the development of biological and gene [[ontologies]] to organize and query biological data. It also plays a role in the analysis of gene and protein expression and regulation. Bioinformatic tools aid in comparing, analyzing, interpreting genetic and genomic data and in the understanding of evolutionary aspects of molecular biology. At a more integrative level, it helps analyze and catalogue the biological pathways and networks that are an important part of [[systems biology]]. In [[structural biology]], it aids in the simulation and modeling of DNA,<ref name=":0">{{cite journal | vauthors = Sim AY, Minary P, Levitt M | title = Modeling nucleic acids | journal = Current Opinion in Structural Biology | volume = 22 | issue = 3 | pages = 273–8 | date = June 2012 | pmid = 22538125 | pmc = 4028509 | doi = 10.1016/j.sbi.2012.03.012 }}</ref> RNA,<ref name=":0" /><ref>{{cite journal | vauthors = Dawson WK, Maciejczyk M, Jankowska EJ, Bujnicki JM | title = Coarse-grained modeling of RNA 3D structure | journal = Methods | volume = 103 | pages = 138–56 | date = July 2016 | pmid = 27125734 | doi = 10.1016/j.ymeth.2016.04.026 | doi-access = free }}</ref> proteins<ref>{{cite journal | vauthors = Kmiecik S, Gront D, Kolinski M, Wieteska L, Dawid AE, Kolinski A | title = Coarse-Grained Protein Models and Their Applications | journal = Chemical Reviews | volume = 116 | issue = 14 | pages = 7898–936 | date = July 2016 | pmid = 27333362 | doi = 10.1021/acs.chemrev.6b00163 | doi-access = free }}</ref> as well as biomolecular interactions.<ref>{{cite book | vauthors = Wong KC |year=2016 |title=Computational Biology and Bioinformatics: Gene Regulation |publisher=CRC Press/Taylor & Francis Group |isbn=978-1-4987-2497-5 }}</ref><ref>{{cite journal | vauthors = Joyce AP, Zhang C, Bradley P, Havranek JJ | title = Structure-based modeling of protein: DNA specificity | journal = Briefings in Functional Genomics | volume = 14 | issue = 1 | pages = 39–49 | date = January 2015 | pmid = 25414269 | pmc = 4366589 | doi = 10.1093/bfgp/elu044 | doi-access = free }}</ref><ref>{{Cite book | vauthors = Spiga E, Degiacomi MT, Dal Peraro M |date=2014 |chapter=New Strategies for Integrative Dynamic Modeling of Macromolecular Assembly | veditors = Karabencheva-Christova T |title=Biomolecular Modelling and Simulations |series=Advances in Protein Chemistry and Structural Biology |volume=96 |pages=77–111 |publisher=Academic Press |doi=10.1016/bs.apcsb.2014.06.008 |pmid=25443955 |isbn=978-0-12-800013-7 }}</ref><ref>{{cite journal | vauthors = Ciemny M, Kurcinski M, Kamel K, Kolinski A, Alam N, Schueler-Furman O, Kmiecik S | title = Protein-peptide docking: opportunities and challenges | journal = Drug Discovery Today | volume = 23 | issue = 8 | pages = 1530–1537 | date = August 2018 | pmid = 29733895 | doi = 10.1016/j.drudis.2018.05.006 | doi-access = free }}</ref>
+Image and [[signal processing]] allow the extraction of useful results from large amounts of raw data. It aids in sequencing and annotating genomes and their observed [[mutation]]s. Bioinformatics includes [[text mining]] of biological literature and the development of biological and gene [[ontologies]] to organize and query biological data. It also plays a role in the analysis of gene and protein expression and regulation. Bioinformatic tools aid in comparing, analyzing, and interpreting genetic and genomic data and in the understanding of evolutionary aspects of molecular biology. At a more integrative level, it helps analyze and catalogue the biological pathways and networks that are an important part of [[systems biology]]. In [[structural biology]], it aids in the simulation and modeling of DNA,<ref name=":0">{{cite journal | vauthors = Sim AY, Minary P, Levitt M | title = Modeling nucleic acids | journal = Current Opinion in Structural Biology | volume = 22 | issue = 3 | pages = 273–8 | date = June 2012 | pmid = 22538125 | pmc = 4028509 | doi = 10.1016/j.sbi.2012.03.012 }}</ref> RNA,<ref name=":0" /><ref name=":1">{{Cite journal |last1=Welch |first1=Lonnie |last2=Lewitter |first2=Fran |last3=Schwartz |first3=Russell |date=2014 |title=Bioinformatics Curriculum Guidelines: Toward a Definition of Core Competencies |journal=PLOS Comput Biol |volume=10 |issue=3 |article-number=e1003496 |doi=10.1371/journal.pcbi.1003496 |pmid=24603430 |pmc=3945096 |bibcode=2014PLSCB..10E3496W |doi-access=free }}</ref> proteins<ref>{{cite journal | vauthors = Kmiecik S, Gront D, Kolinski M, Wieteska L, Dawid AE, Kolinski A | title = Coarse-Grained Protein Models and Their Applications | journal = Chemical Reviews | volume = 116 | issue = 14 | pages = 7898–936 | date = July 2016 | pmid = 27333362 | doi = 10.1021/acs.chemrev.6b00163 | bibcode = 2016ChRv..116.7898K | doi-access = free }}</ref> as well as biomolecular interactions.<ref>{{cite book | vauthors = Wong KC |year=2016 |title=Computational Biology and Bioinformatics: Gene Regulation |publisher=CRC Press/Taylor & Francis Group |isbn=978-1-4987-2497-5 }}</ref><ref>{{cite journal | vauthors = Joyce AP, Zhang C, Bradley P, Havranek JJ | title = Structure-based modeling of protein: DNA specificity | journal = Briefings in Functional Genomics | volume = 14 | issue = 1 | pages = 39–49 | date = January 2015 | pmid = 25414269 | pmc = 4366589 | doi = 10.1093/bfgp/elu044 | doi-access = free }}</ref><ref>{{Cite book | vauthors = Spiga E, Degiacomi MT, Dal Peraro M |date=2014 |chapter=New Strategies for Integrative Dynamic Modeling of Macromolecular Assembly | veditors = Karabencheva-Christova T |title=Biomolecular Modelling and Simulations |series=Advances in Protein Chemistry and Structural Biology |volume=96 |pages=77–111 |publisher=Academic Press |doi=10.1016/bs.apcsb.2014.06.008 |pmid=25443955 |isbn=978-0-12-800013-7 |url=https://durham-repository.worktribe.com/output/1372599 }}</ref><ref>{{cite journal | vauthors = Ciemny M, Kurcinski M, Kamel K, Kolinski A, Alam N, Schueler-Furman O, Kmiecik S | title = Protein-peptide docking: opportunities and challenges | journal = Drug Discovery Today | volume = 23 | issue = 8 | pages = 1530–1537 | date = August 2018 | pmid = 29733895 | doi = 10.1016/j.drudis.2018.05.006 | doi-access = free }}</ref>
-== History ==
+==History==
-The first definition of the term ''bioinformatics'' was coined by [[Paulien Hogeweg]] and [[Ben Hesper]] in 1970, to refer to the study of information processes in biotic systems.<ref>{{cite journal |last1=Ouzounis |first1=C. A. |last2=Valencia |first2=A. |date=2003 |title=Early bioinformatics: the birth of a discipline—a personal view |journal=Bioinformatics |volume=19 |issue=17 |pages=2176–2190 | pmid=14630646 | doi=10.1093/bioinformatics/btg309| doi-access=free}}</ref><ref name="Hogeweg2011">{{cite journal |vauthors=Hogeweg P |title=The Roots of Bioinformatics in Theoretical Biology |journal=PLOS Computational Biology |volume=7 |issue=3 |pages=e1002021 |date=2011 |pmid=21483479 |pmc=3068925 | doi=10.1371/journal.pcbi.1002021 | bibcode = 2011PLSCB...7E2021H | doi-access = free }}</ref><ref>{{Cite journal| vauthors = Hesper B, Hogeweg P |year=1970|title=BIO-INFORMATICA: een werkconcept |trans-title=BIO-INFORMATICS: a working concept |language=nl |journal=Het Kameleon|volume=1 |issue=6| pages=28–29}}</ref><ref>{{cite arXiv |vauthors=Hesper B, Hogeweg P |eprint=2111.11832v1 |title=Bio-informatics: a working concept. A translation of "Bio-informatica: een werkconcept" by B. Hesper and P. Hogeweg |date=2021 |class=q-bio.OT}}</ref><ref>{{cite journal |vauthors = Hogeweg P |title=Simulating the growth of cellular forms |journal=Simulation |volume=31 |issue=3 |pages=90–96 |year=1978 |doi=10.1177/003754977803100305 |s2cid=61206099 }}</ref> This definition placed bioinformatics as a field parallel to [[biochemistry]] (the study of chemical processes in biological systems).<ref name="Hogeweg2011" />
+The first definition of the term ''bioinformatics'' was coined by [[Paulien Hogeweg]] and [[Ben Hesper]] in 1970, to refer to the study of information processes in biotic systems.<ref>{{cite journal |last1=Ouzounis |first1=C. A. |last2=Valencia |first2=A. |date=2003 |title=Early bioinformatics: the birth of a discipline—a personal view |journal=Bioinformatics |volume=19 |issue=17 |pages=2176–2190 | pmid=14630646 | doi=10.1093/bioinformatics/btg309| doi-access=free}}</ref><ref name="Hogeweg2011">{{cite journal |vauthors=Hogeweg P |title=The Roots of Bioinformatics in Theoretical Biology |journal=PLOS Computational Biology |volume=7 |issue=3 |article-number=e1002021 |date=2011 |pmid=21483479 |pmc=3068925 | doi=10.1371/journal.pcbi.1002021 | bibcode = 2011PLSCB...7E2021H | doi-access = free }}</ref><ref>{{Cite journal| vauthors = Hesper B, Hogeweg P |year=1970|title=BIO-INFORMATICA: een werkconcept |trans-title=BIO-INFORMATICS: a working concept |language=nl |journal=Het Kameleon|volume=1 |issue=6| pages=28–29}}</ref><ref>{{cite arXiv |vauthors=Hesper B, Hogeweg P |eprint=2111.11832v1 |title=Bio-informatics: a working concept. A translation of "Bio-informatica: een werkconcept" by B. Hesper and P. Hogeweg |date=2021 |class=q-bio.OT}}</ref><ref>{{cite journal |vauthors = Hogeweg P |title=Simulating the growth of cellular forms |journal=Simulation |volume=31 |issue=3 |pages=90–96 |year=1978 |doi=10.1177/003754977803100305 |s2cid=61206099 }}</ref> This definition placed bioinformatics as a field parallel to [[biochemistry]] (the study of chemical processes in biological systems).<ref name="Hogeweg2011" />
 Bioinformatics and computational biology involved the analysis of biological data, particularly DNA, RNA, and protein sequences. The field of bioinformatics experienced explosive growth starting in the mid-1990s, driven largely by the [[Human Genome Project]] and by rapid advances in DNA sequencing technology.{{cn|date=February 2025}}
@@ Line 20: / Line 19: @@
 Analyzing biological data to produce meaningful information involves writing and running software programs that use [[algorithm]]s from [[graph theory]], [[artificial intelligence]], [[soft computing]], [[data mining]], [[image processing]], and [[computer simulation]]. The algorithms in turn depend on theoretical foundations such as [[discrete mathematics]], [[control theory]], [[system theory]], [[information theory]], and [[statistics]].{{cn|date=May 2024}}
-=== Sequences ===
+===Sequences===
 [[File: Example DNA sequence.png|thumbnail|right|Sequences of genetic material are frequently used in bioinformatics and are easier to manage using computers than manually.]]
 [[File:Muscle alignment view.png|thumb|369x369px|These are sequences being compared in a MUSCLE multiple sequence alignment (MSA). Each sequence name (leftmost column) is from various louse species, while the sequences themselves are in the second column.]]
@@ Line 30: / Line 29: @@
 In the 1970s, new techniques for sequencing DNA were applied to bacteriophage MS2 and øX174, and the extended nucleotide sequences were then parsed with informational and statistical algorithms. These studies illustrated that well known features, such as the coding segments and the triplet code, are revealed in straightforward statistical analyses and were the proof of the concept that bioinformatics would be insightful.<ref>{{cite journal | vauthors = Erickson JW, Altman GG |title=A Search for Patterns in the Nucleotide Sequence of the MS2 Genome |journal=Journal of Mathematical Biology |date=1979 |volume=7 |issue=3 |pages=219–230 |doi=10.1007/BF00275725 |s2cid=85199492 }}</ref><ref>{{cite journal | vauthors = Shulman MJ, Steinberg CM, Westmoreland N | title = The coding function of nucleotide sequences can be discerned by statistical analysis | journal = Journal of Theoretical Biology | volume = 88 | issue = 3 | pages = 409–20 | date = February 1981 | pmid = 6456380 | doi = 10.1016/0022-5193(81)90274-5 | bibcode = 1981JThBi..88..409S }}</ref>
-== Goals ==
+==Goals==
 In order to study how normal cellular activities are altered in different disease states, raw biological data must be combined to form a comprehensive picture of these activities. Therefore{{When|date=June 2023}}, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. This also includes nucleotide and [[amino acid sequence]]s, [[protein domain]]s, and [[protein structure]]s.<ref>{{Cite book|title=Essential Bioinformatics|url=https://archive.org/details/essentialbioinfo00xion|url-access=limited| vauthors = Xiong J |publisher=Cambridge University Press|year=2006|isbn=978-0-511-16815-4|location=Cambridge, United Kingdom|pages=[https://archive.org/details/essentialbioinfo00xion/page/n13 4]|via=Internet Archive}}</ref>
@@ Line 37: / Line 36: @@
 * Development of new mathematical algorithms and statistical measures to assess relationships among members of large data sets. For example, there are methods to locate a [[gene]] within a sequence, to predict protein structure and/or function, and to [[Cluster analysis|cluster]] protein sequences into families of related sequences.
-The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other approaches is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include: [[pattern recognition]], [[data mining]], [[machine learning]] algorithms, and [[Biological Data Visualization|visualization]]. Major research efforts in the field include [[sequence alignment]], [[gene finding]], [[genome assembly]], [[drug design]], [[drug discovery]], [[protein structural alignment|protein structure alignment]], [[protein structure prediction]], prediction of [[gene expression]] and [[protein–protein interaction]]s, [[genome-wide association studies]], the modeling of [[evolution]] and [[Cellular model|cell division/mitosis.]]
+The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other approaches is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include: [[pattern recognition]], [[data mining]], [[machine learning in bioinformatics|machine learning algorithms]], and [[Biological Data Visualization|visualization]]. Major research efforts in the field include [[sequence alignment]], [[gene finding]], [[genome assembly]], [[drug design]], [[drug discovery]], [[protein structural alignment|protein structure alignment]], [[protein structure prediction]], prediction of [[gene expression]] and [[protein–protein interaction]]s, [[genome-wide association studies]], the modeling of [[evolution]] and [[Cellular model|cell division/mitosis.]]
 Bioinformatics entails the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the management and analysis of biological data.
@@ Line 79: / Line 78: @@
 Following the goals that the Human Genome Project left to achieve after its closure in 2003, the [[ENCODE]] project was developed by the [[National Human Genome Research Institute]]. This project is a collaborative data collection of the functional elements of the human genome that uses next-generation DNA-sequencing technologies and genomic tiling arrays, technologies able to automatically generate large amounts of data at a dramatically reduced per-base cost but with the same accuracy (base call error) and fidelity (assembly error).
-==== Gene function prediction ====
+====Gene function prediction====
-While genome annotation is primarily based on sequence similarity (and thus [[Homology (biology)|homology]]), other properties of sequences can be used to predict the function of genes. In fact, most ''gene'' function prediction methods focus on ''protein'' sequences as they are more informative and more feature-rich. For instance, the distribution of hydrophobic [[amino acid]]s predicts [[Transmembrane domain|transmembrane segments]] in proteins. However, protein function prediction can also use external information such as gene (or protein) [[Gene expression|expression]] data, [[protein structure]], or [[protein-protein interactions]].<ref>{{cite journal |vauthors=Erdin S, Lisewski AM, Lichtarge O |title=Protein function prediction: towards integration of similarity metrics |journal=Current Opinion in Structural Biology |volume=21 |issue=2 |pages=180–8 |date=April 2011 |pmid=21353529 |pmc=3120633 |doi=10.1016/j.sbi.2011.02.001}}</ref>
+While genome annotation is primarily based on sequence similarity (and thus [[Homology (biology)|homology]]), other properties of sequences can be used to predict the function of genes. In fact, most ''gene'' function prediction methods focus on ''protein'' sequences as they are more informative and more feature-rich. For instance, the distribution of hydrophobic [[amino acid]]s predicts [[Transmembrane domain|transmembrane segments]] in proteins. However, protein function prediction can also use external information such as gene (or protein) [[Gene expression|expression]] data, [[protein structure]], or [[protein–protein interaction]]s.<ref>{{cite journal |vauthors=Erdin S, Lisewski AM, Lichtarge O |title=Protein function prediction: towards integration of similarity metrics |journal=Current Opinion in Structural Biology |volume=21 |issue=2 |pages=180–8 |date=April 2011 |pmid=21353529 |pmc=3120633 |doi=10.1016/j.sbi.2011.02.001}}</ref>
 ===Computational evolutionary biology===
@@ Line 101: / Line 100: @@
 {{main|Pan-genome}}
-Pan genomics is a concept introduced in 2005 by Tettelin and Medini. Pan genome is the complete gene repertoire of a particular [[monophyletic]] taxonomic group. Although initially applied to closely related strains of a species, it can be applied to a larger context like genus, phylum, etc. It is divided in two parts: the Core genome, a set of genes common to all the genomes under study (often housekeeping genes vital for survival), and the Dispensable/Flexible genome: a set of genes not present in all but one or some genomes under study. A bioinformatics tool BPGA can be used to characterize the Pan Genome of bacterial species.<ref>{{cite journal | vauthors = Chaudhari NM, Gupta VK, Dutta C | title = BPGA- an ultra-fast pan-genome analysis pipeline | journal = Scientific Reports | volume = 6 | pages = 24373 | date = April 2016 | pmid = 27071527 | pmc = 4829868 | doi = 10.1038/srep24373 | bibcode = 2016NatSR...624373C }}</ref>
+Pan genomics is a concept introduced in 2005 by Tettelin and Medini. Pan genome is the complete gene repertoire of a particular [[monophyletic]] taxonomic group. Although initially applied to closely related strains of a species, it can be applied to a larger context like genus, phylum, etc. It is divided in two parts: the Core genome, a set of genes common to all the genomes under study (often housekeeping genes vital for survival), and the Dispensable/Flexible genome: a set of genes not present in all but one or some genomes under study. A bioinformatics tool BPGA can be used to characterize the Pan Genome of bacterial species.<ref>{{cite journal | vauthors = Chaudhari NM, Gupta VK, Dutta C | title = BPGA- an ultra-fast pan-genome analysis pipeline | journal = Scientific Reports | volume = 6 | article-number = 24373 | date = April 2016 | pmid = 27071527 | pmc = 4829868 | doi = 10.1038/srep24373 | bibcode = 2016NatSR...624373C }}</ref>
 ===Genetics of disease===
 {{main|Genome-wide association studies}}
-As of 2013, the existence of efficient high-throughput next-generation sequencing technology allows for the identification of cause many different human disorders. Simple [[Mendelian inheritance]] has been observed for over 3,000 disorders that have been identified at the [[Online Mendelian Inheritance in Man]] database, but complex diseases are more difficult. Association studies have found many individual genetic regions that individually are weakly associated with complex diseases (such as [[infertility]],<ref name="Demerec1945">{{cite journal | vauthors = Aston KI | title = Genetic susceptibility to male infertility: news from genome-wide association studies | journal = Andrology | volume = 2 | issue = 3 | pages = 315–21 | date = May 2014 | pmid = 24574159 | doi = 10.1111/j.2047-2927.2014.00188.x | s2cid = 206007180 | doi-access = free }}</ref> [[breast cancer]]<ref name="Véron2013">{{cite journal | vauthors = Véron A, Blein S, Cox DG | title = Genome-wide association studies and the clinic: a focus on breast cancer | journal = Biomarkers in Medicine | volume = 8 | issue = 2 | pages = 287–96 | year = 2014 | pmid = 24521025 | doi = 10.2217/bmm.13.121 }}</ref> and [[Alzheimer's disease]]<ref name="Tosto2013">{{cite journal | vauthors = Tosto G, Reitz C | title = Genome-wide association studies in Alzheimer's disease: a review | journal = Current Neurology and Neuroscience Reports | volume = 13 | issue = 10 | pages = 381 | date = October 2013 | pmid = 23954969 | pmc = 3809844 | doi = 10.1007/s11910-013-0381-0 }}</ref>), rather than a single cause.<ref name="Londin2013">{{Cite book |vauthors=Londin E, Yadav P, Surrey S, Kricka LJ, Fortina P |chapter=Use of linkage analysis, genome-wide association studies, and next-generation sequencing in the identification of disease-causing mutations |title=Pharmacogenomics |volume=1015 |pages=127–46 |year=2013 |pmid=23824853 |doi=10.1007/978-1-62703-435-7_8 |isbn=978-1-62703-434-0 |series=Methods in Molecular Biology }}</ref><ref>{{cite journal | vauthors = Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA | title = Potential etiologic and functional implications of genome-wide association loci for human diseases and traits | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 106 | issue = 23 | pages = 9362–7 | date = June 2009 | pmid = 19474294 | pmc = 2687147 | doi = 10.1073/pnas.0903103106 | doi-access = free | bibcode = 2009PNAS..106.9362H }}</ref> There are currently many challenges to using genes for diagnosis and treatment, such as how we don't know which genes are important, or how stable the choices an algorithm provides.<ref>{{Cite book | vauthors = Hall LO |title=2010 International Conference on System Science and Engineering |chapter=Finding the right genes for disease and prognosis prediction |date=2010 |pages=1–2 |doi=10.1109/ICSSE.2010.5551766|isbn=978-1-4244-6472-2 |s2cid=21622726 }}</ref>
+As of 2013, the existence of efficient high-throughput next-generation sequencing technology allows for the identification of cause many different human disorders. Simple [[Mendelian inheritance]] has been observed for over 3,000 disorders that have been identified at the [[Online Mendelian Inheritance in Man]] database, but complex diseases are more difficult. Association studies have found many individual genetic regions that individually are weakly associated with complex diseases (such as [[infertility]],<ref name="Demerec1945">{{cite journal | vauthors = Aston KI | title = Genetic susceptibility to male infertility: news from genome-wide association studies | journal = Andrology | volume = 2 | issue = 3 | pages = 315–21 | date = May 2014 | pmid = 24574159 | doi = 10.1111/j.2047-2927.2014.00188.x | s2cid = 206007180 | doi-access = free }}</ref> [[breast cancer]]<ref name="Véron2013">{{cite journal | vauthors = Véron A, Blein S, Cox DG | title = Genome-wide association studies and the clinic: a focus on breast cancer | journal = Biomarkers in Medicine | volume = 8 | issue = 2 | pages = 287–96 | year = 2014 | pmid = 24521025 | doi = 10.2217/bmm.13.121 }}</ref> and [[Alzheimer's disease]]<ref name="Tosto2013">{{cite journal | vauthors = Tosto G, Reitz C | title = Genome-wide association studies in Alzheimer's disease: a review | journal = Current Neurology and Neuroscience Reports | volume = 13 | issue = 10 | article-number = 381 | date = October 2013 | pmid = 23954969 | pmc = 3809844 | doi = 10.1007/s11910-013-0381-0 }}</ref>), rather than a single cause.<ref name="Londin2013">{{Cite book |vauthors=Londin E, Yadav P, Surrey S, Kricka LJ, Fortina P |chapter=Use of linkage analysis, genome-wide association studies, and next-generation sequencing in the identification of disease-causing mutations |title=Pharmacogenomics |volume=1015 |pages=127–46 |year=2013 |pmid=23824853 |doi=10.1007/978-1-62703-435-7_8 |isbn=978-1-62703-434-0 |series=Methods in Molecular Biology }}</ref><ref>{{cite journal | vauthors = Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA | title = Potential etiologic and functional implications of genome-wide association loci for human diseases and traits | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 106 | issue = 23 | pages = 9362–7 | date = June 2009 | pmid = 19474294 | pmc = 2687147 | doi = 10.1073/pnas.0903103106 | doi-access = free | bibcode = 2009PNAS..106.9362H }}</ref> There are currently many challenges to using genes for diagnosis and treatment, such as how we don't know which genes are important, or how stable the choices an algorithm provides.<ref>{{Cite book | vauthors = Hall LO |title=2010 International Conference on System Science and Engineering |chapter=Finding the right genes for disease and prognosis prediction |date=2010 |pages=1–2 |doi=10.1109/ICSSE.2010.5551766|isbn=978-1-4244-6472-2 |s2cid=21622726 }}</ref>
 Genome-wide association studies have successfully identified thousands of common genetic variants for complex diseases and traits; however, these common variants only explain a small fraction of heritability.<ref>{{cite journal |last1=Manolio |first1=Teri A. |last2=Collins |first2=Francis S. |last3=Cox |first3=Nancy J. |last4=Goldstein |first4=David B. |last5=Hindorff |first5=Lucia A. |last6=Hunter |first6=David J. |last7=McCarthy |first7=Mark I. |last8=Ramos |first8=Erin M. |last9=Cardon |first9=Lon R. |last10=Chakravarti |first10=Aravinda |last11=Cho |first11=Judy H. |last12=Guttmacher |first12=Alan E. |last13=Kong |first13=Augustine |last14=Kruglyak |first14=Leonid |last15=Mardis |first15=Elaine |last16=Rotimi |first16=Charles N. |last17=Slatkin |first17=Montgomery |last18=Valle |first18=David |last19=Whittemore |first19=Alice S. |last20=Boehnke |first20=Michael |last21=Clark |first21=Andrew G. |last22=Eichler |first22=Evan E. |last23=Gibson |first23=Greg |last24=Haines |first24=Jonathan L. |last25=Mackay |first25=Trudy F. C. |last26=McCarroll |first26=Steven A. |last27=Visscher |first27=Peter M. |title=Finding the missing heritability of complex diseases |journal=Nature |date=October 2009 |volume=461 |issue=7265 |pages=747–753 |doi=10.1038/nature08494|pmid=19812666 |pmc=2831613 |bibcode=2009Natur.461..747M }}</ref> [[rare functional variant|Rare variants]] may account for some of the [[missing heritability]].<ref>{{cite journal |last1=Wainschtein |first1=Pierrick |last2=Jain |first2=Deepti |last3=Zheng |first3=Zhili |last4=Aslibekyan |first4=Stella |last5=Becker |first5=Diane |last6=Bi |first6=Wenjian |last7=Brody |first7=Jennifer |last8=Carlson |first8=Jenna C. |last9=Correa |first9=Adolfo |last10=Du |first10=Margaret Mengmeng |last11=Fernandez-Rhodes |first11=Lindsay |last12=Ferrier |first12=Kendra R. |last13=Graff |first13=Misa |last14=Guo |first14=Xiuqing |last15=He |first15=Jiang |last16=Heard-Costa |first16=Nancy L. |last17=Highland |first17=Heather M. |last18=Hirschhorn |first18=Joel N. |last19=Howard-Claudio |first19=Candace M. |last20=Isasi |first20=Carmen R. |last21=Jackson |first21=Rebecca |last22=Jiang |first22=Jicai |last23=Joehanes |first23=Roby |last24=Justice |first24=Anne E. |last25=Kalyani |first25=Rita R. |last26=Kardia |first26=Sharon |last27=Lange |first27=Ethan |last28=LeBoff |first28=Meryl |last29=Lee |first29=Seunggeun |last30=Li |first30=Xihao |last31=Li |first31=Zilin |last32=Lim |first32=Elise |last33=Lin |first33=Danyu |last34=Lin |first34=Xihong |last35=Liu |first35=Simin |last36=Lu |first36=Yingchang |last37=Manson |first37=JoAnn |last38=Martin |first38=Lisa |last39=McHugh |first39=Caitlin |last40=Mikulla |first40=Julie |last41=Musani |first41=Solomon K. |last42=Ng |first42=Maggie |last43=Nickerson |first43=Deborah |last44=Palmer |first44=Nicholette |last45=Perry |first45=James |last46=Peters |first46=Ulrike |last47=Preuss |first47=Michael |last48=Qi |first48=Qibin |last49=Raffield |first49=Laura |last50=Rasmussen-Torvik |first50=Laura |last51=Reiner |first51=Alex |last52=Russell |first52=Emily M. |last53=Sitlani |first53=Colleen |last54=Smith |first54=Jennifer |last55=Spracklen |first55=Cassandra N. |last56=Wang |first56=Tao |last57=Wang |first57=Zhe |last58=Wessel |first58=Jennifer |last59=Xu |first59=Hanfei |last60=Yaser |first60=Mohammad |last61=Yoneyama |first61=Sachiko |last62=Young |first62=Kendra A. |last63=Zhang |first63=Jingwen |last64=Zhang |first64=Xinruo |last65=Zhou |first65=Hufeng |last66=Zhu |first66=Xiaofeng |last67=Zoellner |first67=Sebastian |last68=Abe |first68=Namiko |last69=Abecasis |first69=Gonçalo |last70=Aguet |first70=Francois |last71=Almasy |first71=Laura |last72=Alonso |first72=Alvaro |last73=Ament |first73=Seth |last74=Anderson |first74=Peter |last75=Anugu |first75=Pramod |last76=Applebaum-Bowden |first76=Deborah |last77=Ardlie |first77=Kristin |last78=Arking |first78=Dan |last79=Ashley-Koch |first79=Allison |last80=Assimes |first80=Tim |last81=Auer |first81=Paul |last82=Avramopoulos |first82=Dimitrios |last83=Ayas |first83=Najib |last84=Balasubramanian |first84=Adithya |last85=Barnard |first85=John |last86=Barnes |first86=Kathleen |last87=Barr |first87=R. 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C. |last318=Rasmussen-Torvik |first318=Laura |last319=Ratan |first319=Aakrosh |last320=Reed |first320=Robert |last321=Reeves |first321=Catherine |last322=Regan |first322=Elizabeth |last323=Robillard |first323=Rebecca |last324=Robine |first324=Nicolas |last325=Roden |first325=Dan |last326=Roselli |first326=Carolina |last327=Ruczinski |first327=Ingo |last328=Runnels |first328=Alexi |last329=Russell |first329=Pamela |last330=Ruuska |first330=Sarah |last331=Ryan |first331=Kathleen |last332=Sabino |first332=Ester Cerdeira |last333=Saleheen |first333=Danish |last334=Salimi |first334=Shabnam |last335=Salvi |first335=Sejal |last336=Salzberg |first336=Steven |last337=Sandow |first337=Kevin |last338=Sankaran |first338=Vijay G. |last339=Santibanez |first339=Jireh |last340=Schwander |first340=Karen |last341=Schwartz |first341=David |last342=Sciurba |first342=Frank |last343=Seidman |first343=Christine |last344=Seidman |first344=Jonathan |last345=Sériès |first345=Frédéric |last346=Sheehan |first346=Vivien |last347=Sherman |first347=Stephanie L. |last348=Shetty |first348=Amol |last349=Shetty |first349=Aniket |last350=Sheu |first350=Wayne Hui-Heng |last351=Shoemaker |first351=M. Benjamin |last352=Silver |first352=Brian |last353=Silverman |first353=Edwin |last354=Skomro |first354=Robert |last355=Smith |first355=Albert Vernon |last356=Smith |first356=Josh |last357=Smith |first357=Nicholas |last358=Smith |first358=Tanja |last359=Smoller |first359=Sylvia |last360=Snively |first360=Beverly |last361=Snyder |first361=Michael |last362=Sofer |first362=Tamar |last363=Sotoodehnia |first363=Nona |last364=Stilp |first364=Adrienne M. |last365=Storm |first365=Garrett |last366=Streeten |first366=Elizabeth |last367=Su |first367=Jessica Lasky |last368=Sung |first368=Yun Ju |last369=Sylvia |first369=Jody |last370=Szpiro |first370=Adam |last371=Taliun |first371=Daniel |last372=Tang |first372=Hua |last373=Taub |first373=Margaret |last374=Taylor |first374=Matthew |last375=Taylor |first375=Simeon |last376=Telen |first376=Marilyn |last377=Thornton |first377=Timothy A. |last378=Threlkeld |first378=Machiko |last379=Tinker |first379=Lesley |last380=Tirschwell |first380=David |last381=Tishkoff |first381=Sarah |last382=Tiwari |first382=Hemant |last383=Tong |first383=Catherine |last384=Tracy |first384=Russell |last385=Tsai |first385=Michael |last386=Vaidya |first386=Dhananjay |last387=Van Den Berg |first387=David |last388=VandeHaar |first388=Peter |last389=Vrieze |first389=Scott |last390=Walker |first390=Tarik |last391=Wallace |first391=Robert |last392=Walts |first392=Avram |last393=Wang |first393=Fei Fei |last394=Wang |first394=Heming |last395=Wang |first395=Jiongming |last396=Watson |first396=Karol |last397=Watt |first397=Jennifer |last398=Weinstock |first398=Joshua |last399=Weir |first399=Bruce |last400=Weiss |first400=Scott T. |last401=Weng |first401=Lu-Chen |last402=Wessel |first402=Jennifer |last403=Williams |first403=Kayleen |last404=Williams |first404=L. Keoki |last405=Wilson |first405=Carla |last406=Winterkorn |first406=Lara |last407=Wong |first407=Quenna |last408=Wu |first408=Joseph |last409=Xu |first409=Huichun |last410=Yang |first410=Ivana |last411=Yu |first411=Ketian |last412=Zekavat |first412=Seyedeh Maryam |last413=Zhang |first413=Yingze |last414=Zhao |first414=Snow Xueyan |last415=Zhu |first415=Xiaofeng |last416=Ziv |first416=Elad |last417=Zody |first417=Michael |last418=Zoellner |first418=Sebastian |last419=Atkinson |first419=Elizabeth |last420=Ballantyne |first420=Christie |last421=Bao |first421=Wei |last422=Bhattacharya |first422=Romit |last423=Bielak |first423=Larry |last424=Bis |first424=Joshua |last425=Bodea |first425=Corneliu |last426=Brody |first426=Jennifer |last427=Cade |first427=Brian |last428=Calvo |first428=Sarah |last429=Carlson |first429=Jenna |last430=Chang |first430=I-Shou |last431=Cho |first431=So Mi |last432=de Vries |first432=Paul |last433=Diallo |first433=Ana F. |last434=Do |first434=Ron |last435=Dron |first435=Jacqueline |last436=Elliott |first436=Amanda |last437=Finucane |first437=Hilary |last438=Floyd |first438=Caitlin |last439=Ganna |first439=Andrea |last440=Gong |first440=Dawei |last441=Graham |first441=Sarah |last442=Haas |first442=Mary |last443=Haring |first443=Bernhard |last444=Heemann |first444=Scott |last445=Himes |first445=Blanca |last446=Jarvik |first446=Gail |last447=Jiang |first447=Jicai |last448=Joehanes |first448=Roby |last449=Joseph |first449=Paule Valery |last450=Jun |first450=Goo |last451=Kalyani |first451=Rita |last452=Kanai |first452=Masahiro |last453=Kathiresan |first453=Sekar |last454=Khera |first454=Amit |last455=Khetarpal |first455=Sumeet |last456=Klarin |first456=Derek |last457=Koyama |first457=Satoshi |last458=Kral |first458=Brian |last459=Lange |first459=Leslie |last460=Lemaitre |first460=Rozenn |last461=Li |first461=Changwei |last462=Lu |first462=Yingchang |last463=Martin |first463=Lisa |last464=Mathias |first464=Rasika |last465=Mathur |first465=Ravi |last466=McGarvey |first466=Stephen |last467=McLenithan |first467=John |last468=Miller |first468=Amy |last469=Mootha |first469=Vamsi |last470=Moran |first470=Andrew |last471=Nakao |first471=Tetsushi |last472=O'Connell |first472=Jeff |last473=O'Donnell |first473=Christopher |last474=Palmer |first474=Nicholette |last475=Paruchuri |first475=Kaavya |last476=Patel |first476=Aniruddh |last477=Peloso |first477=Gina |last478=Pettinger |first478=Mary |last479=Peyser |first479=Patricia |last480=Pirruccello |first480=James |last481=Psaty |first481=Bruce |last482=Reiner |first482=Alex |last483=Rich |first483=Stephen |last484=Rosenthal |first484=Samantha |last485=Rotter |first485=Jerome |last486=Smith |first486=Jennifer |last487=Sunyaev |first487=Shamil R. |last488=Surakka |first488=Ida |last489=Sztalryd |first489=Carole |last490=Trinder |first490=Mark |last491=Uddin |first491=Md Mesbah |last492=Urbut |first492=Sarah |last493=Van Buren |first493=Eric |last494=Verbanck |first494=Marie |last495=Von Holle |first495=Ann |last496=Wang |first496=Yuxuan |last497=Wiggins |first497=Kerri |last498=Wilkins |first498=John |last499=Willer |first499=Cristen |last500=Wilson |first500=James |last501=Wolford |first501=Brooke |last502=Yanek |first502=Lisa |last503=Yu |first503=Zhi |last504=Zaghloul |first504=Norann |last505=Zhang |first505=Jingwen |last506=Zhou |first506=Ying |last507=Rotter |first507=Jerome I. |last508=Willer |first508=Cristen J. |last509=Natarajan |first509=Pradeep |last510=Peloso |first510=Gina M. |last511=Lin |first511=Xihong|title=A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies |journal=Nature Methods |date=December 2022 |volume=19 |issue=12 |pages=1599–1611 |doi=10.1038/s41592-022-01640-x|pmid=36303018 |pmc=10008172 |s2cid=243873361 }}</ref><ref>{{cite journal |title=STAARpipeline: an all-in-one rare-variant tool for biobank-scale whole-genome sequencing data |journal=Nature Methods |date=December 2022 |volume=19 |issue=12 |pages=1532–1533 |doi=10.1038/s41592-022-01641-w|pmid=36316564 |s2cid=253246835 }}</ref> Meta-analysis of whole genome sequencing studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes.<ref>{{cite journal |last1=Li |first1=Xihao |last2=Quick |first2=Corbin |last3=Zhou |first3=Hufeng |last4=Gaynor |first4=Sheila M. |last5=Liu |first5=Yaowu |last6=Chen |first6=Han |last7=Selvaraj |first7=Margaret Sunitha |last8=Sun |first8=Ryan |last9=Dey |first9=Rounak |last10=Arnett |first10=Donna K. |last11=Bielak |first11=Lawrence F. |last12=Bis |first12=Joshua C. |last13=Blangero |first13=John |last14=Boerwinkle |first14=Eric |last15=Bowden |first15=Donald W. |last16=Brody |first16=Jennifer A. |last17=Cade |first17=Brian E. |last18=Correa |first18=Adolfo |last19=Cupples |first19=L. 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@@ Line 114: / Line 113: @@
 {{main|Oncogenomics}}
-In [[cancer]], the genomes of affected cells are rearranged in complex or unpredictable ways. In addition to [[single-nucleotide polymorphism]] arrays identifying [[point mutation]]s that cause cancer, [[oligonucleotide]] microarrays can be used to identify chromosomal gains and losses (called [[comparative genomic hybridization]]). These detection methods generate [[terabyte]]s of data per experiment.<ref>Tsourakakis, C. E., Tolliver, D., Tsiarli, M. A., Shackney, S. E., & Schwartz, R. (2010). CGHTRIMMER: Discretizing noisy array CGH data. arXiv. https://doi.org/10.48550/arxiv.1002.4438</ref> The data is often found to contain considerable variability, or [[noise]], and thus [[Hidden Markov model]] and change-point analysis methods are being developed to infer real [[copy number]] changes.<ref>Yau, C., Mouradov, D., Jorissen, R. N., Colella, S., Mirza, G., Steers, G., Harris, A., Ragoussis, J., Sieber, O. M., Holmes, C. C., & Silver, A. (2008). A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data. PLoS Computational Biology, 4(3), e1000052. https://doi.org/10.1371/journal.pcbi.0030052</ref>
+In [[cancer]], the genomes of affected cells are rearranged in complex or unpredictable ways. In addition to [[single-nucleotide polymorphism]] arrays identifying [[point mutation]]s that cause cancer, [[oligonucleotide]] microarrays can be used to identify chromosomal gains and losses (called [[comparative genomic hybridization]]). These detection methods generate [[terabyte]]s of data per experiment.<ref>{{cite arXiv | last1=Tsourakakis | first1=Charalampos E. | last2=Tolliver | first2=David | last3=Tsiarli | first3=Maria A. | last4=Shackney | first4=Stanley | last5=Schwartz | first5=Russell | title=CGHTRIMMER: Discretizing noisy Array CGH Data | date=2010 | class=q-bio.GN | eprint=1002.4438 }}</ref> The data is often found to contain considerable variability, or [[noise]], and thus [[Hidden Markov model]] and change-point analysis methods are being developed to infer real [[copy number]] changes.<ref>{{cite journal
+ | last1 = Yau
+ | first1 = Christopher
+ | last2 = Mouradov
+ | first2 = Dmitri
+ | last3 = Jorissen
+ | first3 = Robert N.
+ | last4 = Colella
+ | first4 = Stefano
+ | last5 = Mirza
+ | first5 = Ghazala K.
+ | last6 = Steers
+ | first6 = Graham
+ | last7 = Harris
+ | first7 = Adrian
+ | last8 = Ragoussis
+ | first8 = Jiannis
+ | last9 = Sieber
+ | first9 = Oliver M.
+ | last10 = Holmes
+ | first10 = Chris C.
+ | title = A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data
+ | journal = Genome Biology
+ | year = 2010
+ | volume = 11
+ | number = 9
+ | pages = R92
+ | doi = 10.1186/gb-2010-11-9-r92
+ | pmid = 20804551
+ | doi-access = free
+ | pmc = 2941741
+ }}
+</ref>
-Two important principles can be used to identify cancer by mutations in the [[exome]]. First, cancer is a disease of accumulated somatic mutations in genes. Second, cancer contains driver mutations which need to be distinguished from passengers.<ref>{{cite journal | vauthors = Vazquez M, de la Torre V, Valencia A | title = Chapter 14: Cancer genome analysis | journal = PLOS Computational Biology | volume = 8 | issue = 12 | pages = e1002824 | date = 2012-12-27 | pmid = 23300415 | pmc = 3531315 | doi = 10.1371/journal.pcbi.1002824 | bibcode = 2012PLSCB...8E2824V | doi-access = free }}</ref>
+Two important principles can be used to identify cancer by mutations in the [[exome]]. First, cancer is a disease of accumulated somatic mutations in genes. Second, cancer contains driver mutations which need to be distinguished from passengers.<ref>{{cite journal | vauthors = Vazquez M, de la Torre V, Valencia A | title = Chapter 14: Cancer genome analysis | journal = PLOS Computational Biology | volume = 8 | issue = 12 | article-number = e1002824 | date = 2012-12-27 | pmid = 23300415 | pmc = 3531315 | doi = 10.1371/journal.pcbi.1002824 | bibcode = 2012PLSCB...8E2824V | doi-access = free }}</ref>
 Further improvements in bioinformatics could allow for classifying types of cancer by analysis of cancer driven mutations in the genome. Furthermore, tracking of patients while the disease progresses may be possible in the future with the sequence of cancer samples. Another type of data that requires novel informatics development is the analysis of [[lesion]]s found to be recurrent among many tumors.<ref>{{cite book | vauthors = Hye-Jung EC, Jaswinder K, Martin K, Samuel AA, Marco AM | veditors = Dellaire G, Berman JN, Arceci RJ | title= Cancer Genomics |date=2014 |publisher=Academic Press |location=Boston (US) |isbn=978-0-12-396967-5 |pages=13–30 |chapter=Second-Generation Sequencing for Cancer Genome Analysis |doi=10.1016/B978-0-12-396967-5.00002-5}}</ref>
@@ Line 133: / Line 164: @@
 [[Gene regulation]] is a complex process where a signal, such as an extracellular signal such as a [[hormone]], eventually leads to an increase or decrease in the activity of one or more [[protein]]s. Bioinformatics techniques have been applied to explore various steps in this process.
-For example, gene expression can be regulated by nearby elements in the genome. Promoter analysis involves the identification and study of [[sequence motif]]s in the DNA surrounding the protein-coding region of a gene. These motifs influence the extent to which that region is transcribed into mRNA. [[Enhancer (genetics)|Enhancer]] elements far away from the promoter can also regulate gene expression, through three-dimensional looping interactions. These interactions can be determined by bioinformatic analysis of [[chromosome conformation capture]] experiments.
+For example, gene expression can be regulated by nearby elements in the genome. Promoter analysis involves the identification and study of [[sequence motif]]s in the DNA surrounding the [[protein-coding region]] of a gene. These motifs influence the extent to which that region is transcribed into mRNA. [[Enhancer (genetics)|Enhancer]] elements far away from the promoter can also regulate gene expression, through three-dimensional looping interactions. These interactions can be determined by bioinformatic analysis of [[chromosome conformation capture]] experiments.
 Expression data can be used to infer gene regulation: one might compare [[microarray]] data from a wide variety of states of an organism to form hypotheses about the genes involved in each state. In a single-cell organism, one might compare stages of the [[cell cycle]], along with various stress conditions (heat shock, starvation, etc.). [[cluster analysis|Clustering algorithms]] can be then applied to expression data to determine which genes are co-expressed. For example, the upstream regions (promoters) of co-expressed genes can be searched for over-represented [[regulatory elements]]. Examples of clustering algorithms applied in gene clustering are [[k-means clustering]], [[self-organizing map]]s (SOMs), [[hierarchical clustering]], and [[consensus clustering]] methods.
@@ Line 144: / Line 175: @@
 ===Protein localization===
-Finding the location of proteins allows us to predict what they do. This is called [[protein function prediction]]. For instance, if a protein is found in the [[Cell nucleus|nucleus]] it may be involved in [[gene regulation]] or [[RNA splicing|splicing]]. By contrast, if a protein is found in [[mitochondria]], it may be involved in [[Cellular respiration|respiration]] or other [[Metabolism|metabolic processes]]. There are well developed [[protein subcellular localization prediction]] resources available, including protein subcellular location databases, and prediction tools.<ref>{{Cite web |url=https://www.proteinatlas.org/humancell |title=The human cell |website=www.proteinatlas.org |access-date=2017-10-02 |archive-date=2 October 2017 |archive-url=https://web.archive.org/web/20171002215345/https://www.proteinatlas.org/humancell |url-status=live }}</ref><ref>{{cite journal | vauthors = Thul PJ, Åkesson L, Wiking M, Mahdessian D, Geladaki A, Ait Blal H, Alm T, Asplund A, Björk L, Breckels LM, Bäckström A, Danielsson F, Fagerberg L, Fall J, Gatto L, Gnann C, Hober S, Hjelmare M, Johansson F, Lee S, Lindskog C, Mulder J, Mulvey CM, Nilsson P, Oksvold P, Rockberg J, Schutten R, Schwenk JM, Sivertsson Å, Sjöstedt E, Skogs M, Stadler C, Sullivan DP, Tegel H, Winsnes C, Zhang C, Zwahlen M, Mardinoglu A, Pontén F, von Feilitzen K, Lilley KS, Uhlén M, Lundberg E | title = A subcellular map of the human proteome | journal = Science | volume = 356 | issue = 6340 | pages = eaal3321 | date = May 2017 | pmid = 28495876 | doi = 10.1126/science.aal3321 | s2cid = 10744558 }}</ref>
+Finding the location of proteins allows us to predict what they do. This is called [[protein function prediction]]. For instance, if a protein is found in the [[Cell nucleus|nucleus]] it may be involved in [[gene regulation]] or [[RNA splicing|splicing]]. By contrast, if a protein is found in [[mitochondria]], it may be involved in [[Cellular respiration|respiration]] or other [[Metabolism|metabolic processes]]. There are well developed [[protein subcellular localization prediction]] resources available, including protein subcellular location databases, and prediction tools.<ref>{{Cite web |url=https://www.proteinatlas.org/humancell |title=The human cell |website=www.proteinatlas.org |access-date=2017-10-02 |archive-date=2 October 2017 |archive-url=https://web.archive.org/web/20171002215345/https://www.proteinatlas.org/humancell |url-status=live }}</ref><ref>{{cite journal | vauthors = Thul PJ, Åkesson L, Wiking M, Mahdessian D, Geladaki A, Ait Blal H, Alm T, Asplund A, Björk L, Breckels LM, Bäckström A, Danielsson F, Fagerberg L, Fall J, Gatto L, Gnann C, Hober S, Hjelmare M, Johansson F, Lee S, Lindskog C, Mulder J, Mulvey CM, Nilsson P, Oksvold P, Rockberg J, Schutten R, Schwenk JM, Sivertsson Å, Sjöstedt E, Skogs M, Stadler C, Sullivan DP, Tegel H, Winsnes C, Zhang C, Zwahlen M, Mardinoglu A, Pontén F, von Feilitzen K, Lilley KS, Uhlén M, Lundberg E | title = A subcellular map of the human proteome | journal = Science | volume = 356 | issue = 6340 | article-number = eaal3321 | date = May 2017 | pmid = 28495876 | doi = 10.1126/science.aal3321 | bibcode = 2017Sci...356l3321T | s2cid = 10744558 }}</ref>
 ===Nuclear organization of chromatin===
 {{main|Nuclear organization}}
-Data from high-throughput [[chromosome conformation capture]] experiments, such as [[Hi-C (experiment)]] and [[ChIA-PET]], can provide information on the three-dimensional structure and [[nuclear organization]] of [[chromatin]]. Bioinformatic challenges in this field include partitioning the genome into domains, such as [[Topologically Associating Domain]]s (TADs), that are organised together in three-dimensional space.<ref>{{cite journal | vauthors = Ay F, Noble WS | title = Analysis methods for studying the 3D architecture of the genome | journal = Genome Biology | volume = 16 | issue = 1 | pages = 183 | date = September 2015 | pmid = 26328929 | pmc = 4556012 | doi = 10.1186/s13059-015-0745-7 | doi-access = free }}</ref>
+Data from high-throughput [[chromosome conformation capture]] experiments, such as [[Hi-C (experiment)]] and [[ChIA-PET]], can provide information on the three-dimensional structure and [[nuclear organization]] of [[chromatin]]. Bioinformatic challenges in this field include partitioning the genome into domains, such as [[Topologically Associating Domain]]s (TADs), that are organised together in three-dimensional space.<ref>{{cite journal | vauthors = Ay F, Noble WS | title = Analysis methods for studying the 3D architecture of the genome | journal = Genome Biology | volume = 16 | issue = 1 | article-number = 183 | date = September 2015 | pmid = 26328929 | pmc = 4556012 | doi = 10.1186/s13059-015-0745-7 | doi-access = free }}</ref>
 ==Structural bioinformatics==<!-- this is specific enough, compared to general knowledge in previous paragraph, that it would be nice to have a source or example -->
@@ Line 157: / Line 188: @@
 Finding the structure of proteins is an important application of bioinformatics. The Critical Assessment of Protein Structure Prediction (CASP) is an open competition where worldwide research groups submit protein models for evaluating unknown protein models.<ref>{{Cite journal|title=Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XIII|year=2019 |pmc=6927249 |last1=Kryshtafovych |first1=A. |last2=Schwede |first2=T. |last3=Topf |first3=M. |last4=Fidelis |first4=K. |last5=Moult |first5=J. |journal=Proteins |volume=87 |issue=12 |pages=1011–1020 |doi=10.1002/prot.25823 |pmid=31589781 }}</ref><ref>{{Cite web |title=Home - CASP14 |url=https://predictioncenter.org/casp14/ |access-date=2023-06-12 |website=predictioncenter.org |archive-date=30 January 2023 |archive-url=https://web.archive.org/web/20230130200222/https://predictioncenter.org/casp14/ |url-status=live }}</ref>
-=== Amino acid sequence ===
+===Amino acid sequence===
 The linear [[amino acid]] sequence of a protein is called the [[primary structure]]. The primary structure can be easily determined from the sequence of [[codons]] on the DNA gene that codes for it. In most proteins, the primary structure uniquely determines the 3-dimensional structure of a protein in its native environment. An exception is the misfolded [[prion]] protein involved in [[bovine spongiform encephalopathy]]. This structure is linked to the function of the protein. Additional structural information includes the ''[[secondary structure|secondary]]'', ''[[tertiary structure|tertiary]]'' and ''[[quaternary structure|quaternary]]'' structure. A viable general solution to the prediction of the function of a protein remains an open problem. Most efforts have so far been directed towards heuristics that work most of the time.{{citation needed|date=July 2015}}
-=== Homology ===
+===Homology===
 In the genomic branch of bioinformatics, homology is used to predict the function of a gene: if the sequence of gene ''A'', whose function is known, is homologous to the sequence of gene ''B,'' whose function is unknown, one could infer that B may share A's function. In structural bioinformatics, homology is used to determine which parts of a protein are important in structure formation and interaction with other proteins. [[Homology modeling]] is used to predict the structure of an unknown protein from existing homologous proteins.
@@ Line 180: / Line 211: @@
 ===Molecular interaction networks===
 {{main|Protein–protein interaction prediction|interactome}}
-[[File:The protein interaction network of Treponema pallidum.png|200px|thumbnail|right|Interactions between proteins are frequently visualized and analyzed using networks. This network is made up of protein–protein interactions from ''[[Treponema pallidum]]'', the causative agent of [[syphilis]] and other diseases.<ref>{{cite journal | vauthors = Titz B, Rajagopala SV, Goll J, Häuser R, McKevitt MT, Palzkill T, Uetz P | title = The binary protein interactome of Treponema pallidum--the syphilis spirochete | journal = PLOS ONE | volume = 3 | issue = 5 | pages = e2292 | date = May 2008 | pmid = 18509523 | pmc = 2386257 | doi = 10.1371/journal.pone.0002292 | bibcode = 2008PLoSO...3.2292T | veditors = Hall N | doi-access = free }}</ref>]]
+[[File:The protein interaction network of Treponema pallidum.png|200px|thumbnail|right|Interactions between proteins are frequently visualized and analyzed using networks. This network is made up of protein–protein interactions from ''[[Treponema pallidum]]'', the causative agent of [[syphilis]] and other diseases.<ref>{{cite journal | vauthors = Titz B, Rajagopala SV, Goll J, Häuser R, McKevitt MT, Palzkill T, Uetz P | title = The binary protein interactome of Treponema pallidum--the syphilis spirochete | journal = PLOS ONE | volume = 3 | issue = 5 | article-number = e2292 | date = May 2008 | pmid = 18509523 | pmc = 2386257 | doi = 10.1371/journal.pone.0002292 | bibcode = 2008PLoSO...3.2292T | veditors = Hall N | doi-access = free }}</ref>]]
 Tens of thousands of three-dimensional protein structures have been determined by [[X-ray crystallography]] and [[protein nuclear magnetic resonance spectroscopy]] (protein NMR) and a central question in structural bioinformatics is whether it is practical to predict possible protein–protein interactions only based on these 3D shapes, without performing [[protein–protein interaction]] experiments. A variety of methods have been developed to tackle the [[protein–protein docking]] problem, though it seems that there is still much work to be done in this field.
@@ Line 218: / Line 249: @@
 ===Ontologies and data integration===
-Biological ontologies are [[directed acyclic graph]]s of [[controlled vocabularies]]. They create categories for biological concepts and descriptions so they can be easily analyzed with computers. When categorised in this way, it is possible to gain added value from holistic and integrated analysis.{{Citation needed|date=June 2023}}
+Biological ontologies are [[directed acyclic graph]]s of [[controlled vocabularies]]. They create categories for biological concepts and descriptions so they can be easily analyzed with computers. When categorised in this way, it is possible to gain added value from holistic and integrated analysis.<ref>{{Cite journal |last=Groß |first=Anika |last2=Pruski |first2=Cédric |last3=Rahm |first3=Erhard |date=2016-01-01 |title=Evolution of biomedical ontologies and mappings: Overview of recent approaches |url=https://www.sciencedirect.com/science/article/pii/S2001037016300319 |journal=Computational and Structural Biotechnology Journal |volume=14 |pages=333–340 |doi=10.1016/j.csbj.2016.08.002 |issn=2001-0370|doi-access=free |pmc=5018063 }}</ref>
 The [[OBO Foundry]] was an effort to standardise certain ontologies. One of the most widespread is the [[Gene ontology]] which describes gene function. There are also ontologies which describe phenotypes.
@@ Line 254: / Line 285: @@
 Basic bioinformatics services are classified by the [[European Bioinformatics Institute|EBI]] into three categories: [[Sequence alignment software|SSS]] (Sequence Search Services), [[Multiple sequence alignment|MSA]] (Multiple Sequence Alignment), and [[#Sequence analysis|BSA]] (Biological Sequence Analysis).<ref>{{Cite book | vauthors = Nisbet R, Elder IV J, Miner G |title=Handbook of Statistical Analysis and Data Mining Applications |chapter-url=https://books.google.com/books?id=U5np34a5fmQC&q=bioinformatics%20service%20categories%20EBI&pg=PA328|publisher=Academic Press |year=2009 |page=328 |chapter=Bioinformatics |isbn=978-0-08-091203-5 }}</ref> The availability of these [[Service-orientation|service-oriented]] bioinformatics resources demonstrate the applicability of web-based bioinformatics solutions, and range from a collection of standalone tools with a common data format under a single web-based interface, to integrative, distributed and extensible [[bioinformatics workflow management systems]].
-==== Bioinformatics workflow management systems ====
+====Bioinformatics workflow management systems====
 {{main|Bioinformatics workflow management systems}}
@@ Line 266: / Line 297: @@
 <!-- This is like saying there are different kinds of websites, some of which are convert-PNG-to-PDF.com, and others which are Wikipedia. Plus a lot of jargon. Consider cutting down further.  -->
-=== BioCompute and BioCompute Objects ===
+===BioCompute and BioCompute Objects===
-In 2014, the [[US Food and Drug Administration]] sponsored a conference held at the [[National Institutes of Health]] Bethesda Campus to discuss reproducibility in bioinformatics.<ref>{{Cite web|url=https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm389561.htm|title=Advancing Regulatory Science – Sept. 24–25, 2014 Public Workshop: Next Generation Sequencing Standards|author=Office of the Commissioner|website=www.fda.gov|language=en|access-date=2017-11-30|archive-date=14 November 2017|archive-url=https://web.archive.org/web/20171114200347/https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm389561.htm|url-status=dead}}</ref> Over the next three years, a consortium of stakeholders met regularly to discuss what would become BioCompute paradigm.<ref>{{cite journal | vauthors = Simonyan V, Goecks J, Mazumder R | title = Biocompute Objects-A Step towards Evaluation and Validation of Biomedical Scientific Computations | journal = PDA Journal of Pharmaceutical Science and Technology | volume = 71 | issue = 2 | pages = 136–146 | date = 2017 | pmid = 27974626 | pmc = 5510742 | doi = 10.5731/pdajpst.2016.006734 }}</ref> These stakeholders included representatives from government, industry, and academic entities. Session leaders represented numerous branches of the FDA and NIH Institutes and Centers, non-profit entities including the [[Human Variome Project]] and the [[European Federation for Medical Informatics]], and research institutions including [[Stanford]], the [[New York Genome Center]], and the [[George Washington University]].
+In 2014, the [[US Food and Drug Administration]] sponsored a conference held at the [[National Institutes of Health]] Bethesda Campus to discuss reproducibility in bioinformatics.<ref>{{Cite web|url=https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm389561.htm|title=Advancing Regulatory Science – Sept. 24–25, 2014 Public Workshop: Next Generation Sequencing Standards|author=Office of the Commissioner|website=www.fda.gov|language=en|access-date=2017-11-30|archive-date=14 November 2017|archive-url=https://web.archive.org/web/20171114200347/https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm389561.htm}}</ref> Over the next three years, a consortium of stakeholders met regularly to discuss what would become BioCompute paradigm.<ref>{{cite journal | vauthors = Simonyan V, Goecks J, Mazumder R | title = Biocompute Objects-A Step towards Evaluation and Validation of Biomedical Scientific Computations | journal = PDA Journal of Pharmaceutical Science and Technology | volume = 71 | issue = 2 | pages = 136–146 | date = 2017 | pmid = 27974626 | pmc = 5510742 | doi = 10.5731/pdajpst.2016.006734 }}</ref> These stakeholders included representatives from government, industry, and academic entities. Session leaders represented numerous branches of the FDA and NIH Institutes and Centers, non-profit entities including the [[Human Variome Project]] and the [[European Federation for Medical Informatics]], and research institutions including [[Stanford]], the [[New York Genome Center]], and the [[George Washington University]].
-It was decided that the BioCompute paradigm would be in the form of digital 'lab notebooks' which allow for the reproducibility, replication, review, and reuse, of bioinformatics protocols. This was proposed to enable greater continuity within a research group over the course of normal personnel flux while furthering the exchange of ideas between groups. The US FDA funded this work so that information on pipelines would be more transparent and accessible to their regulatory staff.<ref>{{Cite web|url=https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm491893.htm|title=Advancing Regulatory Science – Community-based development of HTS standards for validating data and computation and encouraging interoperability|author=Office of the Commissioner|website=www.fda.gov|language=en|access-date=2017-11-30|archive-date=26 January 2018|archive-url=https://web.archive.org/web/20180126133504/https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm491893.htm|url-status=dead}}</ref>
+It was decided that the BioCompute paradigm would be in the form of digital 'lab notebooks' which allow for the reproducibility, replication, review, and reuse, of bioinformatics protocols. This was proposed to enable greater continuity within a research group over the course of normal personnel flux while furthering the exchange of ideas between groups. The US FDA funded this work so that information on pipelines would be more transparent and accessible to their regulatory staff.<ref>{{Cite web|url=https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm491893.htm|title=Advancing Regulatory Science – Community-based development of HTS standards for validating data and computation and encouraging interoperability|author=Office of the Commissioner|website=www.fda.gov|language=en|access-date=2017-11-30|archive-date=26 January 2018|archive-url=https://web.archive.org/web/20180126133504/https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm491893.htm}}</ref>
-In 2016, the group reconvened at the NIH in Bethesda and discussed the potential for a [[BioCompute Object]], an instance of the BioCompute paradigm. This work was copied as both a "standard trial use" document and a preprint paper uploaded to bioRxiv. The BioCompute object allows for the JSON-ized record to be shared among employees, collaborators, and regulators.<ref>{{cite journal | vauthors = Alterovitz G, Dean D, Goble C, Crusoe MR, Soiland-Reyes S, Bell A, Hayes A, Suresh A, Purkayastha A, King CH, Taylor D, Johanson E, Thompson EE, Donaldson E, Morizono H, Tsang H, Vora JK, Goecks J, Yao J, Almeida JS, Keeney J, Addepalli K, Krampis K, Smith KM, Guo L, Walderhaug M, Schito M, Ezewudo M, Guimera N, Walsh P, Kahsay R, Gottipati S, Rodwell TC, Bloom T, Lai Y, Simonyan V, Mazumder R | title = Enabling precision medicine via standard communication of HTS provenance, analysis, and results | journal = PLOS Biology | volume = 16 | issue = 12 | pages = e3000099 | date = December 2018 | pmid = 30596645 | doi = 10.1371/journal.pbio.3000099 | pmc = 6338479 | doi-access = free }}</ref><ref>{{Citation|title=BioCompute Object (BCO) project is a collaborative and community-driven framework to standardize HTS computational data. 1. BCO Specification Document: user manual for understanding and creating B.|date=2017-09-03|url=https://github.com/biocompute-objects/HTS-CSRS|publisher=biocompute-objects|access-date=30 November 2017|archive-date=27 June 2018|archive-url=https://web.archive.org/web/20180627081221/https://github.com/biocompute-objects/HTS-CSRS|url-status=live}}</ref>
+In 2016, the group reconvened at the NIH in Bethesda and discussed the potential for a [[BioCompute Object]], an instance of the BioCompute paradigm. This work was copied as both a "standard trial use" document and a preprint paper uploaded to bioRxiv. The BioCompute object allows for the JSON-ized record to be shared among employees, collaborators, and regulators.<ref>{{cite journal | vauthors = Alterovitz G, Dean D, Goble C, Crusoe MR, Soiland-Reyes S, Bell A, Hayes A, Suresh A, Purkayastha A, King CH, Taylor D, Johanson E, Thompson EE, Donaldson E, Morizono H, Tsang H, Vora JK, Goecks J, Yao J, Almeida JS, Keeney J, Addepalli K, Krampis K, Smith KM, Guo L, Walderhaug M, Schito M, Ezewudo M, Guimera N, Walsh P, Kahsay R, Gottipati S, Rodwell TC, Bloom T, Lai Y, Simonyan V, Mazumder R | title = Enabling precision medicine via standard communication of HTS provenance, analysis, and results | journal = PLOS Biology | volume = 16 | issue = 12 | article-number = e3000099 | date = December 2018 | pmid = 30596645 | doi = 10.1371/journal.pbio.3000099 | pmc = 6338479 | doi-access = free }}</ref><ref>{{Citation|title=BioCompute Object (BCO) project is a collaborative and community-driven framework to standardize HTS computational data. 1. BCO Specification Document: user manual for understanding and creating B.|date=2017-09-03|url=https://github.com/biocompute-objects/HTS-CSRS|publisher=biocompute-objects|access-date=30 November 2017|archive-date=27 June 2018|archive-url=https://web.archive.org/web/20180627081221/https://github.com/biocompute-objects/HTS-CSRS|url-status=live}}</ref>
 ==Education platforms==
-While bioinformatics is taught as an in-person [[master's degree]] at many universities, there are many other methods and technologies available to learn and obtain certification in the subject. The computational nature of bioinformatics lends it to [[Educational technology|computer-aided and online learning]].<ref>{{Cite journal |last=Campbell |first=A. Malcolm |date=2003-06-01 |title=Public Access for Teaching Genomics, Proteomics, and Bioinformatics |journal=Cell Biology Education |volume=2 |issue=2 |pages=98–111 |doi=10.1187/cbe.03-02-0007 |pmc=162192 |pmid=12888845}}</ref><ref>{{Cite journal |last=Arenas |first=Miguel |date=September 2021 |title=General considerations for online teaching practices in bioinformatics in the time of COVID -19 |journal=Biochemistry and Molecular Biology Education |language=en |volume=49 |issue=5 |pages=683–684 |doi=10.1002/bmb.21558 |issn=1470-8175 |pmc=8426940 |pmid=34231941}}</ref> Software platforms designed to teach bioinformatics concepts and methods include [[Rosalind (education platform)|Rosalind]] and online courses offered through the [[Swiss Institute of Bioinformatics]] Training Portal. The [[Canadian Bioinformatics Workshops]] provides videos and slides from training workshops on their website under a [[Creative Commons]] license. The 4273π project or 4273pi project<ref>{{cite journal | vauthors = Barker D, Ferrier DE, Holland PW, Mitchell JB, Plaisier H, Ritchie MG, Smart SD | title = 4273π: bioinformatics education on low cost ARM hardware | journal = BMC Bioinformatics | volume = 13 | pages = 522 | date = August 2013 | pmid = 23937194 | pmc = 3751261 | doi = 10.1186/1471-2105-14-243 | doi-access = free }}</ref> also offers open source educational materials for free. The course runs on low cost [[Raspberry Pi]] computers and has been used to teach adults and school pupils.<ref>{{cite journal | vauthors = Barker D, Alderson RG, McDonagh JL, Plaisier H, Comrie MM, Duncan L, Muirhead GT, Sweeney SD |title=University-level practical activities in bioinformatics benefit voluntary groups of pupils in the last 2 years of school |journal=International Journal of STEM Education |date=2015 |volume=2 |issue=17 |doi=10.1186/s40594-015-0030-z | s2cid = 256396656 |hdl=10023/7704 |hdl-access=free | doi-access = free }}</ref><ref>{{cite journal | vauthors = McDonagh JL, Barker D, Alderson RG | title = Bringing computational science to the public | journal = SpringerPlus | volume = 5 | issue = 259 | pages = 259 | date = 2016 | pmid = 27006868 | pmc = 4775721 | doi = 10.1186/s40064-016-1856-7 | doi-access = free }}</ref> 4273 is actively developed by a consortium of academics and research staff who have run research level bioinformatics using Raspberry Pi computers and the 4273π operating system.<ref>{{cite journal | vauthors = Robson JF, Barker D | title = Comparison of the protein-coding gene content of Chlamydia trachomatis and Protochlamydia amoebophila using a Raspberry Pi computer | journal = BMC Research Notes | volume = 8 | issue = 561 | pages = 561 | date = October 2015 | pmid = 26462790 | pmc = 4604092 | doi = 10.1186/s13104-015-1476-2 | doi-access = free }}</ref><ref>{{cite journal | vauthors = Wreggelsworth KM, Barker D | title = A comparison of the protein-coding genomes of two green sulphur bacteria, Chlorobium tepidum TLS and Pelodictyon phaeoclathratiforme BU-1 | journal = BMC Research Notes | volume = 8 | issue = 565 | pages = 565 | date = October 2015 | pmid = 26467441 | pmc = 4606965 | doi = 10.1186/s13104-015-1535-8 | doi-access = free }}</ref>
+While bioinformatics is taught as an in-person [[master's degree]] at many universities, there are many other methods and technologies available to learn and obtain certification in the subject. The computational nature of bioinformatics lends it to [[Educational technology|computer-aided and online learning]].<ref>{{Cite journal |last=Campbell |first=A. Malcolm |date=2003-06-01 |title=Public Access for Teaching Genomics, Proteomics, and Bioinformatics |journal=Cell Biology Education |volume=2 |issue=2 |pages=98–111 |doi=10.1187/cbe.03-02-0007 |pmc=162192 |pmid=12888845}}</ref><ref>{{Cite journal |last=Arenas |first=Miguel |date=September 2021 |title=General considerations for online teaching practices in bioinformatics in the time of COVID -19 |journal=Biochemistry and Molecular Biology Education |language=en |volume=49 |issue=5 |pages=683–684 |doi=10.1002/bmb.21558 |issn=1470-8175 |pmc=8426940 |pmid=34231941}}</ref> Software platforms designed to teach bioinformatics concepts and methods include [[Rosalind (education platform)|Rosalind]] and online courses offered through the [[Swiss Institute of Bioinformatics]] Training Portal. The [[Canadian Bioinformatics Workshops]] provides videos and slides from training workshops on their website under a [[Creative Commons]] license. The 4273π project or 4273pi project<ref>{{cite journal | vauthors = Barker D, Ferrier DE, Holland PW, Mitchell JB, Plaisier H, Ritchie MG, Smart SD | title = 4273π: bioinformatics education on low cost ARM hardware | journal = BMC Bioinformatics | volume = 13 | page = 522 | date = August 2013 | article-number = 243 | pmid = 23937194 | pmc = 3751261 | doi = 10.1186/1471-2105-14-243 | doi-access = free }}</ref> also offers open source educational materials for free. The course runs on low cost [[Raspberry Pi]] computers and has been used to teach adults and school pupils.<ref>{{cite journal | vauthors = Barker D, Alderson RG, McDonagh JL, Plaisier H, Comrie MM, Duncan L, Muirhead GT, Sweeney SD |title=University-level practical activities in bioinformatics benefit voluntary groups of pupils in the last 2 years of school |journal=International Journal of STEM Education |date=2015 |volume=2 |issue=17 |article-number=17 |doi=10.1186/s40594-015-0030-z | s2cid = 256396656 |hdl=10023/7704 |hdl-access=free | doi-access = free }}</ref><ref>{{cite journal | vauthors = McDonagh JL, Barker D, Alderson RG | title = Bringing computational science to the public | journal = SpringerPlus | volume = 5 | issue = 259 | article-number = 259 | date = 2016 | pmid = 27006868 | pmc = 4775721 | doi = 10.1186/s40064-016-1856-7 | doi-access = free }}</ref> 4273 is actively developed by a consortium of academics and research staff who have run research level bioinformatics using Raspberry Pi computers and the 4273π operating system.<ref>{{cite journal | vauthors = Robson JF, Barker D | title = Comparison of the protein-coding gene content of Chlamydia trachomatis and Protochlamydia amoebophila using a Raspberry Pi computer | journal = BMC Research Notes | volume = 8 | issue = 561 | article-number = 561 | date = October 2015 | pmid = 26462790 | pmc = 4604092 | doi = 10.1186/s13104-015-1476-2 | doi-access = free }}</ref><ref>{{cite journal | vauthors = Wreggelsworth KM, Barker D | title = A comparison of the protein-coding genomes of two green sulphur bacteria, Chlorobium tepidum TLS and Pelodictyon phaeoclathratiforme BU-1 | journal = BMC Research Notes | volume = 8 | issue = 565 | article-number = 565 | date = October 2015 | pmid = 26467441 | pmc = 4606965 | doi = 10.1186/s13104-015-1535-8 | doi-access = free }}</ref>
 [[MOOC]] platforms also provide online certifications in bioinformatics and related disciplines, including [[Coursera]]'s Bioinformatics Specialization at the [[University of California, San Diego]], Genomic Data Science Specialization at [[Johns Hopkins University]], and [[EdX]]'s Data Analysis for Life Sciences XSeries at [[Harvard University]].
 ==Conferences==
-There are several large conferences that are concerned with bioinformatics. Some of the most notable examples are [[Intelligent Systems for Molecular Biology]] (ISMB), [[European Conference on Computational Biology]] (ECCB), and [[Research in Computational Molecular Biology]] (RECOMB).
+There are several large conferences that are concerned with bioinformatics. Some of the most notable examples are [[European Conference on Computational Biology]] (ECCB), [[Intelligent Systems for Molecular Biology]] (ISMB), [https://psb.stanford.edu Pacific Symposium on Biocomputing] (PSB), and [[Research in Computational Molecular Biology]] (RECOMB).
-== See also ==
+==See also==
 {{Portal|Biology|Evolutionary biology}}
 {{Columns-list|colwidth=30em|
@@ Line 306: / Line 337: @@
 }}
-== References ==
+==References==
 {{Reflist}}
-== Further reading ==
+==Further reading==
 <!-- It's possible that some of these were used as the original sources for the article. -->
 {{Library resources box}}
 {{refbegin|35em}}
-* Sehgal et al. : Structural, phylogenetic and docking studies of D-amino acid oxidase activator(DAOA ), a candidate schizophrenia gene. Theoretical Biology and Medical Modelling 2013 10 :3.
+* Sehgal et al. : Structural, phylogenetic and docking studies of D-amino acid oxidase activator (DAOA), a candidate schizophrenia gene. Theoretical Biology and Medical Modelling 2013 10 :3.
 * Achuthsankar S Nair [http://print.achuth.googlepages.com/BINFTutorialV5.0CSI07.pdf Computational Biology & Bioinformatics – A gentle Overview] {{Webarchive|url=https://web.archive.org/web/20081216212125/http://print.achuth.googlepages.com/BINFTutorialV5.0CSI07.pdf |date=16 December 2008 }}, Communications of Computer Society of India, January 2007
 * [[Srinivas Aluru|Aluru, Srinivas]], ed. ''Handbook of Computational Molecular Biology''. Chapman & Hall/Crc, 2006. {{ISBN|1-58488-406-1}} (Chapman & Hall/Crc Computer and Information Science Series)
-* Baldi, P and Brunak, S, ''Bioinformatics: The Machine Learning Approach'', 2nd edition. MIT Press, 2001. {{ISBN|0-262-02506-X}}
+* Baldi, P and [[Søren Brunak|Brunak]], S, ''Bioinformatics: The Machine Learning Approach'', 2nd edition. MIT Press, 2001. {{ISBN|0-262-02506-X}}
 * Barnes, M.R. and Gray, I.C., eds., ''Bioinformatics for Geneticists'', first edition. Wiley, 2003. {{ISBN|0-470-84394-2}}
-* Baxevanis, A.D. and Ouellette, B.F.F., eds., ''Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins'', third edition. Wiley, 2005. {{ISBN|0-471-47878-4}}
+* Baxevanis, A.D. and [[Francis Ouellette|Ouellette]], B.F.F., eds., ''Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins'', third edition. Wiley, 2005. {{ISBN|0-471-47878-4}}
 * Baxevanis, A.D., Petsko, G.A., Stein, L.D., and Stormo, G.D., eds., ''[[Current Protocols]] in Bioinformatics''. Wiley, 2007. {{ISBN|0-471-25093-7}}
 * Cristianini, N. and Hahn, M. [http://www.computational-genomics.net/ ''Introduction to Computational Genomics''] {{Webarchive|url=https://web.archive.org/web/20090104042023/http://www.computational-genomics.net/ |date=4 January 2009 }}, Cambridge University Press, 2006. ({{ISBN|9780521671910}} |{{ISBN|0-521-67191-4}})
@@ Line 331: / Line 362: @@
 * Stevens, Hallam, ''Life Out of Sequence: A Data-Driven History of Bioinformatics'', Chicago: The University of Chicago Press, 2013, {{ISBN|9780226080208}}
 * Tisdall, James. "Beginning Perl for Bioinformatics" O'Reilly, 2001. {{ISBN|0-596-00080-4}}
-* [http://www.nap.edu/catalog/11480.html Catalyzing Inquiry at the Interface of Computing and Biology (2005) CSTB report] {{Webarchive|url=https://web.archive.org/web/20070128222920/http://www.nap.edu/catalog/11480.html |date=28 January 2007 }}
+* [https://www.nap.edu/catalog/11480.html Catalyzing Inquiry at the Interface of Computing and Biology (2005) CSTB report] {{Webarchive|url=https://web.archive.org/web/20070128222920/http://www.nap.edu/catalog/11480.html |date=28 January 2007 }}
-* [http://www.nap.edu/catalog/2121.html Calculating the Secrets of Life: Contributions of the Mathematical Sciences and computing to Molecular Biology (1995)] {{Webarchive|url=https://web.archive.org/web/20080706035211/http://www.nap.edu/catalog/2121.html |date=6 July 2008 }}
+* [https://www.nap.edu/catalog/2121.html Calculating the Secrets of Life: Contributions of the Mathematical Sciences and computing to Molecular Biology (1995)] {{Webarchive|url=https://web.archive.org/web/20080706035211/http://www.nap.edu/catalog/2121.html |date=6 July 2008 }}
 * [https://web.archive.org/web/20071222091912/http://ocw.mit.edu/OcwWeb/Biology/7-91JSpring2004/LectureNotes/index.htm Foundations of Computational and Systems Biology MIT Course]
-* [http://compbio.mit.edu/6.047/ Computational Biology: Genomes, Networks, Evolution Free MIT Course] {{Webarchive|url=https://web.archive.org/web/20130408034631/http://compbio.mit.edu/6.047/ |date=8 April 2013 }}
+* [https://compbio.mit.edu/6.047/ Computational Biology: Genomes, Networks, Evolution Free MIT Course] {{Webarchive|url=https://web.archive.org/web/20130408034631/http://compbio.mit.edu/6.047/ |date=8 April 2013 }}
 {{Refend}}
-== External links ==
+==External links==
 {{Spoken Wikipedia|En-Bioinformatics.ogg|date=2013-09-20}}
 {{Wiktionary|bioinformatics}}
@@ Line 343: / Line 374: @@
 {{Commons category}}
 <!-- Please use the talk page to propose any additions to this section. If you do not do this, the link will almost certainly be deleted. Also, do not list bioinformatics research groups or centers.-->
-* [http://expasy.org Bioinformatics Resource Portal (SIB)]
+* [https://expasy.org Bioinformatics Resource Portal (SIB)]
 {{Bioinformatics}}

Bioinformatics: Difference between revisions

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